Home » SYNTHESIS – Senior Seminar – The Use of Technology in Early Intervention

SYNTHESIS – Senior Seminar – The Use of Technology in Early Intervention

Read the following 3 articles and synthesize (Combine the ideas of all three sources into one overall point – DO NOT SUMMARIZE)  them into 1 and half page word document. Also, write a well elaborated question from each reading. Keep in mind the following points when working on this task:

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*Questions must be original, thought and not easily found in the articles.

*Follow APA Rules

*Use proper citations

*Use  PAST TENSE when discussing the articles  (Research already took place)

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*DO NOT USE the following words: Me, you, I, we.

*Refer to the articles by their AUTHORS (year of publication) 

*DO NOT USE the article name or words first, second, or third.



Two Factor Model of ASD Symptoms

One of the key factors in determining whether an individual has Autism Spectrum Disorder (ASD) is in their social and communication skills. Individuals who are diagnosed with ASD have delayed joint attention, eye gazing, and other social interactions such as pointing (Swain et al., 2014).

Joint attention is an important social skill to master because it is a building block for developing theory of mind which, helps us to understand other’s perspectives. Korhonen et al. (2014) found that individuals with autism have impaired joint attention. However, some did not show impairment in joint attention, which lead to evidence that suggests there are different trajectories for joint attention. One suggestion as to why Korhonen et al. (2014) found mixed results, is that there is evidence that joint attention may not be directly linked to individuals with ASD since they were unable to find a difference in joint attention between ASD and developmentally delayed (DD) individuals. Another suggestion for the mixed results, is individual interest in the task vary. Research has found that while individualized studies are beneficial in detecting personal potential and abilities, it would be difficult to generalize the study in order to further research to ASD as a whole (Korhonen et al., 2014). In addition to joint attention, atypical gaze shifts is a distinguishing factor in individuals with ASD. Swain et al. (2014) found the main difference between typically developing (TD) and ASD individuals in the first 12 months of life is in gaze shifts. Individuals that were diagnosed with ASD earlier had lower scores on positive affect, joint attention, and gaze shifts, however those diagnosed later differed from typically developing (TD) only in gaze shifts. It is not until 24 months that later onset ASD individuals significantly differ from their TD peers, by displaying lower positive affect and gestures (Swain et al., 2014). These findings may lead to other ASD trajectories.

Another defining characteristic of ASD is the excess of restrictive patterns of interest and repetitive motor movements. These patterns and movements often impaired the individual from completing daily tasks. Like joint attention and gaze shifts, these repetitive movements and patterns of interest have different trajectories (Joseph et al., 2013). Joseph et al. (2013) found that individuals with high cognitive functioning ASD engage in more distinct and specific interests and less in repetitive motor movements than individuals with lower cognitive functioning ASD. Another finding showed that at the age of two, repetitive motor and play patterns were more common than compulsion. By the age of four all these behaviors increased however, repetitive use of specific objects was found to be less frequent in older children than younger children. This finding suggests that the ritualistic behaviors and motor movements may present themselves differently based on the age of the individual (Joseph et al., 2013).

Joseph et al. (2013), Korhornen et al. (2014), and Swain et al. (2014) all defined key characteristics of an ASD individual and explains the different trajectories of each characteristic. The difficulty with the trajectories is that it is specific to each individual, some symptoms may worsen while others remain stable. It is also difficult to generalize finding with small sample sizes (Joseph et al., 2013).

Discussion Questions:

Korhonen et al. (2014) did not use preference-based stimuli to look for joint attention and did not separate high- from low-functioning ASD individuals. Do you think that there could be a difference in level of motivation from each group? If so, how do you think this could change the results?

Swain et al. (2014) found that early and late onset of ASD did not differ in their social skills scores at the age of 12 months. If we know that their social skills do not differ then, is there another factor that would allow diagnosis of late onset ASD to be diagnosed at an earlier point in development?

Joseph et al. (2013) explains that it is difficult to assess the trajectories of ASD with a small sample size however, how do you think that their findings still help advance the research on ASD?

Focus on Autism and Other
Developmental Disabilities
2015, Vol. 30(3) 174 –181
© Hammill Institute on Disabilities 2014
Reprints and permissions:
DOI: 10.1177/1088357614537353


The use of information and communication technologies
(ICTs), such as iPad (Apple Computer Inc., 2012) and tablet
applications, as a platform to assist in the education and
skill development of children with autism spectrum disor-
der (ASD) is a relatively new area of investigation.
Nevertheless, anecdotal evidence supports the potential
usefulness of such applications (Attwood, 2003; Jowett,
Moore, & Anderson, 2012; Ploog, Scharf, Nelson, &
Brooks, 2013). Furthermore, ICTs such as iPad applications
are potentially a time- and cost-effective, innovative, and
widely accessible form of intervention (Abdullah &
Brereton, 2012; Strain, Schwartz, & Barton, 2011). To
assess the potential impact that new generation devices
such as iPads have for children with ASD, it is necessary to
explore the attitudes and behaviors of their parents and the
professionals who work with these children toward this
modality. The attitudes of parents and professionals are
likely to be related to the degree to which children with
ASD are encouraged and supported to use such technology
for the purposes of education, behavior change, and/or
skills development.

As methods of diagnosis for ASD become increasingly
refined, diagnosis can occur as early as 18 months of age.
This has created additional demand for early intervention
for children with ASD. Early intervention typically refers to
a series of individualized programs designed to meet the
developmental needs and goals of the specific child (Ben

Itzchak & Zachor, 2011). Although early intervention has
positive outcomes for children with ASD, the high cost
associated with these therapies place financial pressure on
the health care system and the families of children with
ASD (Bailey, Hebbeler, Scarborough, Spiker, & Mallik,

A number of experimental studies have demonstrated that
ICT-based programs are effective and engaging to children
with ASD (Hutinger, 1996; Ploog et al., 2013; Rajendran &
Mitchell, 2006; Silver & Oakes, 2001; Werry, Dautenhahn,
& Harwin, 2001). A recent review of the literature concluded
that there is accumulating evidence, albeit limited at this
stage, that ICT-based programs can be used in the treatment
and education of children with ASD to enhance social, com-
municative, and language development, and that such tech-
nologies are likely to play a central role in the treatment of
children with ASD in coming years (Ploog et al., 2013). A
number of factors have been postulated to explain the appeal
of ICT for children with ASD: ICT is inherently less socially
threatening than face-to-face interactions (Goodwin, 2008;

537353 FOAXXX10.1177/1088357614537353Focus on Autism and Other Developmental DisabilitiesClark et al.

1Swinburne University of Technology, Hawthorn, Victoria, Australia
2Deakin University, Melbourne, Victoria, Australia

Corresponding Author:
David W. Austin, School of Psychology, Deakin University, 221 Burwood
Highway, Burwood, Melbourne, Victoria, 2134, Australia.
Email: david.austin@deakin.edu.au

Professional and Parental Attitudes
Toward iPad Application Use in
Autism Spectrum Disorder

Megan L. E. Clark, BA1, David W. Austin, PhD2, and Melinda J. Craike, PhD2

This study explored the attitudes of parents and professionals who work with children with autism spectrum disorder
(ASD) toward the utilization of iPads and use of iPad applications by children with ASD. A survey of parents (n = 90) and
professionals (n = 31) assessed information and communication technology (ICT) anxiety and self-efficacy, attitude toward
ICT and iPad applications, and iPad utilization. Both parents and professionals held positive attitudes toward ICT and iPad
use for children with ASD. Parents reported high use of iPads by their children, and professionals reported some, albeit
limited, utilization as part of their practice. These findings suggest that iPad applications are not being used by professionals
to a degree that is consistent with their favorable attitudes toward them. iPad use has been enthusiastically adopted by
many parents; however, there appears a need for training in their use and research to establish an evidence base.

autism spectrum disorder (ASD), information and communication technologies (ICT), intervention, iPad applications,
technology-related anxiety, attitudes, computer self-efficacy, skill level, education, behavior, parents, children, professionals



Clark et al. 175

Rajendran, Mitchell, & Rickards, 2005), the nature of com-
munication is more consistent with the autistic style of learn-
ing and interaction (Rajendran & Mitchell, 2006), and
children with ASD have a strong attraction to, and fascina-
tion for, systems of a mechanical nature, given their inherent
structure and predictable nature.

Technological advances have led to a shift in the use from
more traditional ICT resources such as the computer, to
newer mobile devices such as iPads and tablet computers.
Touch screen devices such as the iPad are becoming a popu-
lar choice for many children (both typically developing and
with ASD) and offer many advantages over traditional
devices; they are compact, portable, reinforcing (Murdock,
Ganz, & Crittendon, 2013), and potentially cost-effective.
Research into the effectiveness of iPad applications to deliver
interventions for children with developmental disabilities is
emerging. A recent systematic review evaluated the use of
iPods, iPads, and related devices to deliver educational pro-
grams for people with developmental disabilities and found
that outcomes were largely positive, suggesting that these
devices are viable technological aids for individuals with
developmental disabilities (Kagohara et al., 2013). For chil-
dren with ASD, exploratory research has examined the effec-
tiveness of the iPad as a communication device (Flores et al.,
2012), in the delivery of video modeling treatment (Jowett
et al., 2012), and a play story to increase dialogue (Murdock
et al., 2013). Research to date on the effectiveness of these
devices, however, is limited; thus, there should be some cau-
tion in their use (Maglione et al., 2012).

The cost-effectiveness of iPad applications contributes
to their attractiveness as a mode of delivery for early inter-
ventions. Applications are low in cost in comparison with
face-to-face educational and therapeutic interventions for
ASD. For example, an in-home intensive Applied
Behavioral Analysis therapy program can cost between
AUS$30,000 and AUS$50,000 per child per annum (Sharpe
& Baker, 2007), making this impractical for use in the pub-
lic health care or educational systems and inaccessible to
many families. More similar to an iPad is an electronic
communication device such as the DynaVox Maestro. This
is available for approximately AUS$12,000. In contrast,
iPad’s retail for less than AUS$1000 and ASD-specific iPad
applications can be purchased from between AUS$0.99
(e.g., “Autism Track”) and AUS$200.00 (e.g.,
“Proloquo2Go”), with several alternatives available at no
cost (e.g., “ABA Flashcards”).

Given the potential of iPad applications to enhance and
increase the delivery of educational and/or therapeutic
interventions to children with ASD, it is time to examine
factors that might influence the uptake of such interven-
tions. Examination of parental and professional attitudes
toward iPads for children with ASD is an important compo-
nent of the uptake of this technology since attitudes are
typically a strong predictor of subsequent behavior (Kadel,

2005; Wang, Ertmer, & Newby, 2004). To better understand
attitudes toward ICT generally, two predictors can be exam-
ined: anxiety toward technology and self-efficacy. Early
negative ICT experiences are detrimental to overall tech-
nology use, creating an exaggerated, negative set of
responses and cognitions about one’s ability to use technol-
ogy (Brown & Inouye, 1978; Cassidy & Eachus, 2002).
Conversely, positive first experiences with ICTs facilitate
the development of positive self-beliefs about capabilities,
associated with an increase in positive attitudes toward
technology. However, even if one encounters a negative
first experience with technology, continual exposure and
assistance can alleviate some of the anxiety and negative
cognitions associated with that experience. An understand-
ing of anxiety and self-efficacy will assist in the prediction
of attitudes and thus behavior toward ICT.

Although there is minimal research on the attitudes of
parents toward the use of iPad applications for their chil-
dren’s development, informal commentaries from parents
suggest that they are generally viewed positively. Parents
express expectations that applications might effectively
enhance their children’s growth, communication, cognition,
fine and gross motor, and social interactive skills through
accessible activities for education and treatment (DeCurtis
& Ferrer, 2011).

Professionals who work with children with ASD recog-
nize the role that technology may play assisting children to
reach therapeutic goals (Attwood, 2003). Despite this, many
professionals have expressed concerns regarding imple-
menting iPad use into lesson plans and therapy sessions
(Gasparini & Culen, 2012). Research in related areas sug-
gests that this anxiety toward iPad use might be attributed to
a lack of confidence in the selection and use of applications,
fearing lack of technological experience and awareness
may inhibit children from gaining the maximum benefit
from this adaptive technology (Hennessy, Ruthven, &
Brindley, 2005).

Educators, support staff, therapists, and parents pro-
foundly influence the assimilation of new technologies into
education and therapeutic intervention (Smith, Caputi, &
Rawstorne, 2000). Therefore, it is important for research to
explore the attitudes of those who work with children with
ASD as well as the children’s parents. Attitudes will likely
affect the extent to which iPad applications are integrated
into therapeutic and educational programs delivered both
by professionals and, in the home, by parents.

Research Aims

To date, there has been little research into the attitudes of
parents and professionals toward the use of iPad applica-
tions by children with ASD. The aims of this exploratory
study were to (a) examine the attitudes of parents and pro-
fessionals (engaged in work with children with ASD)

176 Focus on Autism and Other Developmental Disabilities 30(3)

toward ICT generally and iPad application use specifically,
(b) examine the extent to which children with ASD engage
in iPad application use in the home and also the extent to
which professionals utilize iPads in therapeutic settings,
and (c) examine the extent to which education level, tech-
nology-related anxiety, and self-efficacy predict attitudes
toward ICT generally and iPad application use specifically.


Demographic Information

Parents (n = 90) were asked to provide information regard-
ing their age, level of education completed, and their child’s
age. Professionals (n = 31) were asked to provide informa-
tion on their age and occupation.

The age of parents ranged from 22 to 63 years (Mage = 42
years, SD = 5.75). Most parents had an undergraduate (n =
33, 36.7%) or postgraduate degree (n = 27, 30.0%). The age
of children with ASD ranged from 2 to 12 years (Mage = 7
years, SD = 2.84). See Table 1.

Professionals’ ages ranged from 25 to 65 years (Mage =
39 years, SD = 8.34). They came from a range of back-
grounds with the most common being speech pathologists
(n = 7, 22.6%). See Table 2.


Technology-related self-efficacy, anxiety, and attitudes: Parent
and professional questionnaires. The three domains of the
49-item Computer Technology Use Scale (CTUS; Conrad
& Munro, 2008) were used to measure computer self-
efficacy, technology-related anxiety, and attitudes to

Computer self-efficacy. Items were derived from the
four mediators of self-efficacy including persistence, goal
setting, attribution, and coping strategies. Participants
responded to 11 items indicating their perceived ability to
effectively use different types of technology using a 7-point
Likert-type scale (1 = strongly disagree, 7 = strongly agree).
In separate samples, Conrad and Munro found the psycho-
metric properties of the 11-item computer self-efficacy
domain were satisfactory, ranging from .72 to .76 (Conrad
& Munro, 2008).

Technology-related anxiety. This domain of the CTUS
measured participant anxiety related to technology (Con-
rad & Munro, 2008). Based on the unique two-factor struc-
ture of this domain, all items loading onto factor 1 concern
computer use, whereas factor 2 refers to the use of technol-
ogy generally. Items were purposely intended to measure
both unpleasant and positive emotional states. Participants
respond to the 15 items on a 7-point Likert-type scale (1 =
uncomfortable, 7 = comfortable). In separate samples, the
internal consistency was satisfactory, with the overall alpha
coefficients for the 15 items ranging between .76 and .87
(Conrad & Munro, 2008).

Attitudes toward technology. This domain was included to
determine whether attitudes toward various types of tech-
nologies were positive (e.g., “I can do more things with
technology”) or negative (e.g., “Technology complicates
people’s lives”). Responses were rated using a 7-point Lik-
ert-type scale (1 = strongly disagree, 7 = strongly agree).
The internal consistency of the 10-item attitudes to technol-
ogy domain has been shown to be acceptable across the two
samples, ranging between .70 and .74 (Conrad & Munro,

Attitude toward iPad applications: Parent and professional
questionnaires. The “Attitudes Toward iPad Applications”
subscale comprised the 10 items from the “Attitudes
Toward Technology” subscale of the CTUS (Conrad &
Munro, 2008). In this study, the items were contextualized
to specifically measure attitudes toward iPad application
use, rather than technology use in general. For example,
Question 29 “iPad applications enrich people’s lives” was
a modification of Item 19 from the original scale “Tech-
nology Enriches People’s Lives” (Conrad & Munro, 2008).

Table 1. Parent and Child Demographic Characteristics.

Variable M SD N %

Child 07 2.84
Parent 42 5.75
Did not finish Year 12 19 21.1
Finished Year 12 20 22.2
Undergraduate degree 33 36.7
Postgraduate degree 27 30

Note. Total n = 90.

Table 2. Professional Age and Occupational Characteristics.

Variable M (SD) n %

Age (years) 39 (8.34)
Speech pathologist 7 22.6
Education support worker 4 12.9
Special education teacher 4 12.9
Occupational therapist 4 12.9
Psychologist 4 12.9
Integration aide 3 9.6
Teacher 3 09.6
ABA therapist 2 06.4%

Note. n = 31. ABA = applied behavioral analysis therapy.

Clark et al. 177

Responses were rated on a 7-point Likert-type scale (1 =
strongly disagree, 7 = strongly agree). This new scale
showed good internal consistency with a Cronbach’s alpha
coefficient of .83, comparing favorably with the original
CTUS. This indicated that the modified scale did not suffer
psychometrically; indeed, it showed higher internal consis-
tency than the original.

Frequency and duration of iPad use: Parent questionnaire. This
domain was developed for the current study and included
items to measure frequency of the child’s iPad use (i.e.,
“percentage of time spent engaging with iPad apps in the
past 5 days”) and duration of iPad use (i.e., “provide an esti-
mation of the amount of time your child spent engaging
with iPad apps in the last 5 days”). The parents were also
asked to report on the amount of time (months/years) their
child with ASD had been using an iPad.

Frequency and duration of iPad use: Professional question-
naire. Length of iPad use in months/years was estimated
with the item “Provide an indication of how long you have
been using an iPad as part of your occupation with children
with an AD.” Professionals responded to the following item
reporting an estimation of their total iPad use in the past
working week: “provide an indication of how many days
you have used an iPad as part of your occupation in the past
5 working days.” In an attempt to differentiate the use of
iPad applications for therapy/education and or reinforce-
ment/reward, the following items were included: “How
often were iPad applications of a therapeutic and/or educa-
tional nature used by children with ASD as part of your
occupation” and “How often were iPad applications used
by children with AD as part of your occupation for purposes
other than education/therapy (i.e., reinforcement, reward, or


Parents of children with ASD and professionals working
with children with ASD were recruited through advertise-
ments placed in school or organizational newsletters or on
school/organizational websites. Autism-specific organiza-
tions, early intervention centers, mainstream primary
schools, parent support groups, and special education facili-
ties Australia-wide were contacted during the recruitment
process. All participants were asked to complete an online
questionnaire that took approximately 20 min to complete.

Due to the online administration of the questionnaire,
participants were not required to sign a consent form.
However, all participants were asked to read a brief intro-
duction to the study that included a statement explaining
their consent would be implied through completion of the
questionnaire. The project was approved by an accredited
University Human Research Ethics Committee.

Data Analysis

Descriptive statistics (frequency percentage, M, SD) were
used to analyze sample characteristics, computer self-effi-
cacy, technology-related anxiety, attitude toward technol-
ogy and iPad use, and iPad-related behavior for parents and

Two independent samples t-tests were conducted to
compare general attitudes to technology, as well as attitudes
specifically toward iPad applications, across parents and
professionals. Correlation analyses were performed to
investigate the relationship between attitudes and behaviors
toward iPad applications for both professionals and parents.
Four multiple linear regression analyses were conducted to
explore the extent to which anxiety, self-efficacy, and level
of education (for parents only) predicted Attitudes Toward
Technology and iPad application use for parents and profes-
sionals. “Total attitude toward iPad apps” and “Total atti-
tudes toward technology” were entered as the dependent
variable while education level (for parents only), computer
self-efficacy, and technology-related anxiety were entered
as predictors.


Anxiety, Self-Efficacy, Attitudes, and Behaviors:
Technology and iPad Applications

The attitudes toward technology in general and iPad appli-
cations specifically were favorable among both parents and
professionals. The results of t-tests revealed no significant
difference in mean parent attitudes (M = 45.15, SD = 8.15)
and professional attitudes (M = 46.14, SD = 6.65) toward
general technology use, t(121) = −0.594, p = .55.
Furthermore, there were no significant difference in parent
attitudes (M = 51.73, SD = 9.26) and professional attitudes
(M = 50.62, SD = 9.19) toward iPad application use, t(120)
= 0.565, p = .57. No significant difference in mean technol-
ogy-related anxiety was identified between professionals
and parents, t(113) = −0.838, p = .40, although profession-
als did have a slightly higher (although non-significant)
mean technology-related anxiety (M = 85.83, SD = 11.88)
than parents (M = 83.51, SD = 14.97) Comparisons of mean
computer self-efficacy for parents (M = 49.40, SD = 8.37)
and health professionals (M = 47.54, SD = 8.10) revealed
no significant difference between the groups, t(117) = 1.30,
p = .19.

iPad Application Use

As professionals and parents were asked slightly different
questions in the “Behavior Toward iPad Applications” sub-
scale, the variable “total behavior toward iPad applications”
was computed separately for both groups.

178 Focus on Autism and Other Developmental Disabilities 30(3)

Parental reports of child iPad use. Parental reports of child
iPad use showed that almost half of the children (46%) had
begun using an iPad 12 to 18 months ago, while 30% had
begun using an iPad in the past 6 months. Only a small per-
centage of children in the sample (3%) had never used an
iPad. iPad use was high for children with ASD, with the
mean frequency of use reported as a total of 4.6 days (SD =
1.74) out of the previous 5 days. Child iPad usage was fur-
ther broken down into estimated time spent using the device
across the 5-day period: 22% of parents reported a total of 5
to 6 hr use by their child across the 5-day period. Further-
more, 16% of parents stated their child’s iPad use exceeded
10 hr, with a mean of approximately 2 hr (SD = 2.03) of
iPad use per day.

Professional iPad use. Based on self-report data, profession-
als’ iPad usage was quite irregular: 26% of professionals
had been using an iPad in their work with ASD children for
less than 6 months. Furthermore, 35% of professionals
reported having never used an iPad as part of their occupa-
tion. Although approximately half of the sample reported
limited to no use of iPads, the remaining half of the sample
demonstrated some use of the iPad across three of the five
previous working days, either for therapeutic intervention
(16%) or purposes other than therapy, such as reward, rein-
forcement, and play (16%).

Relationship Between Attitudes and iPad Use

There was a moderate positive, but not significant, relation-
ship between Attitudes Toward iPad Applications and use
of iPad applications (frequency/duration) for professionals,
r(28) = .40, p = .38. Furthermore, a strong positive relation-
ship, r(88) = .57, p = <.001, between iPad application use and Attitudes Toward iPad Applications was found in the parent group. When investigating the association between Attitudes Toward Technology generally and iPad use, a strong positive relationship was identified among parents, r(86) = .52, p = .001. In contrast, a weak positive relation- ship between Attitudes Toward Technology generally and iPad use was found in the professional group, r(28) = .18, p = .348. This association was not significant.

Predictors of Parent and Professional Attitudes

Parental attitudes. Together, the factors “computer self-effi-
cacy,” “technology-related anxiety,” and “highest level of
education” accounted for 39% of the total variance in paren-
tal “Attitudes Toward Technology.” The standardized beta
coefficients reveal technology-related anxiety was the
strongest predictor of parental Attitudes Toward Technol-
ogy and iPads (see Table 3).

An R2 value of .23 indicated that 23% of the total vari-
ance in parents’ “Attitudes Toward iPad Applications” was

explained by “computer self-efficacy,” “technology-related
anxiety,” and “highest level of education” combined.
Standardized beta coefficients indicate that “technology-
related anxiety” was the most significant predictor of paren-
tal Attitudes Toward iPad Applications (see Table 4).

Professional attitudes. Predictors of professionals’ “Attitudes
Toward Technology” revealed that combined “computer
self-efficacy” and “technology-related anxiety” explained
39% of their “Attitudes Toward Technology.” Consistent
with parental attitudes, “technology-related anxiety” was
the strongest predictor of professionals’ Attitudes Toward

An R2 value of .21 indicated that 21% of the total vari-
ance in professional “Attitudes Toward iPad Applications”
was explained by “computer self-efficacy” and “technol-
ogy-related anxiety,” combined. The standardized beta
coefficients showed that “technology-related anxiety” was
the strongest predictor of professionals’ attitudes toward
“iPad applications” (see Tables 5 and 6).


The present study was the first study, to the authors’ knowl-
edge, to examine parental and professional attitudes and
behaviors toward ICT-based support materials generally,
and iPad application use specifically for use by children
with ASD. Our findings indicated that both parents and
professionals held positive attitudes toward ICT and iPad
use and, for parents, positive attitudes were positively

Table 3. Predictors of Parental Attitudes Toward Technology.

Statistic Self-efficacy Anxiety Education

β −0.211** −0.599** −0.198**
SE −0.092** −0.048** −0.659**
R2 = .39

Note. n = 90. Self-efficacy = computer self-efficacy; Education = highest
level of education completed; Anxiety = technology-related anxiety.
*p < .05. **p < .001.

Table 4. Predictors of Parental Attitudes Toward iPad

Statistic Self-efficacy Anxiety Education

β −0.131** −0.501** −0.018**
SE −0.109** −0.062** −0.828**
R2 = .23

Note. n = 90. Self-efficacy = computer self-efficacy; Education = highest
level education completed; Anxiety = technology-related anxiety.
**p < .001.

Clark et al. 179

Table 6. Predictors of Professionals Attitudes Toward iPad

Statistic Self-efficacy Anxiety

β −.037* −0.47**
SE −.099 −0.058
R2 = .21

Note. n = 31. Self-efficacy = computer self-efficacy; Anxiety = technology-
related anxiety.
*p < .05. **p < .001.

Table 5. Predictors of Professionals’ Attitudes Toward

Statistic Self-efficacy Anxiety

β −0.14** 0.65**
SE 0.077** 0.042**
R2 = .39

Note. n = 31. Self-efficacy = computer self-efficacy; Anxiety = technology-
related anxiety.
**p < .001.

associated with extent of use of iPad applications by their
children with ASD. Parents reported a high level of iPad
application use by their children, and professionals incor-
porated some, albeit irregular, use of iPads into their work
with children with ASD. Thus, for professionals, the lower
reported iPad use did not appear to be the result of less
favorable attitudes toward them (or to technology gener-
ally). Surprisingly, technology-related anxiety was the
strongest predictor of positive Attitudes Toward Technology
and iPads for both parents and professionals.

Parents reported a high level of iPad use by their chil-
dren; 22% of parents reported that their child with ASD had
used the iPad for approximately 5 to 6 hr in total across the
past 5-day period. This is the first study to the author’s
knowledge to assess the extent of iPad use by children with
ASD. In the broader area of the use of touch screen devices
for people with disabilities, it has been acknowledged that
the use of portable touch devices is a rapidly growing area
of research (Stephenson & Limbrick, 2013). Research on
the extent and type of iPad use for typically developing
children is scarce; however, a 2011 study conducted in the
United States found that 52% of 0- to 8-year-olds had access
to a new mobile device such as a smartphone, video iPod, or
iPad/tablet. In a typical day, 11% of 0- to 8-year-olds used a
smartphone, video iPod, iPad, or similar device to play
games, watch videos, or use other applications, and these
children spend an average of 43 min a day on such devices
(Common Sense Media, 2011).

Our findings indicated that, despite the lack of strong
evidence confirming the efficacy of iPad applications as an

educational or therapeutic intervention for ASD (Maglione
et al., 2012), parents reported a high uptake of this technol-
ogy. Research investigating alternative treatment methods
in ASD suggests that many parents do not wait for treat-
ments to be empirically supported, with some parents using
a trial and error system of intervention for their child
(Christon, Mackintosh, & Myers, 2010). Parents appear to
adopt a similar approach toward iPad application use for
their children with ASD, with literature in health and dis-
ability reports suggesting that parents have emerged as the
primary driving force behind iPad use in ASD (Australian
Government, 2013). Given this high rate of uptake by par-
ents, it is important to educate them on the appropriate use
of iPad applications for their children with ASD and also to
caution parents regarding the lack of scientific evidence for
many of these applications.

There are several possible explanations for the limited
uptake of iPads by professionals working with children
with ASD. Professionals are trained to wait for evidence-
based intervention to ensure best practice (Hennessy et al.,
2005), and the lack of scientific validation of iPad applica-
tions may explain their limited use. A lack of confidence
may also explain the limited use of these devices. In the
formal education context, Price (2011) proposed that lack of
training and unfamiliarity with ICT devices such as the iPad
leaves educators and clinicians lacking confidence when
attempting to integrate the use of applications into a lesson
plan or “one-on-one” therapy session.

Training or professional development in the use of ICT
devices for professionals working with children with ASD
may increase their confidence in the utilization of iPad
applications for the delivery of education and goal-based
intervention in the future. As well as a dearth of available
specialist training and evidence of the efficacy of iPad
applications, other factors such as time pressures and an
inflexibility of existing curricula/intervention models to
incorporate the use of iPad applications are potential factors
which could explain the limited iPad use reported by pro-
fessionals. Nevertheless, further research is required to
clearly identify the predictors of use of iPad applications by
professional educators, therapists, and clinicians.

Education level, computer self-efficacy, and technology-
related anxiety predicted positive Attitudes Toward
Technology use in general by parents. These findings sup-
port the expectation that higher levels of education and
higher computer self-efficacy would result in more positive
attitudes toward general technology use (Conrad & Munro,

Technology-related anxiety was identified as the most
significant predictor of both attitudes to technology gener-
ally, and iPad applications specifically for both parents and
professionals. The direction of this relationship suggested
that higher technology-related anxiety was associated with
more positive attitudes toward both technology generally

180 Focus on Autism and Other Developmental Disabilities 30(3)

and iPad applications specifically. These results are incon-
sistent with previous research that has identified high tech-
nology-related anxiety as being associated with negative
Attitudes Toward Technology (Cassidy & Eachus, 2002;
Conrad & Munro, 2008; Smith et al., 2000). Despite the
technology-related anxiety, the positive Attitudes Toward
Technology may be attributed to parental first-hand experi-
ences of the benefits of technology for children with ASD.

It is important to note that the scale used in this study to
investigate technology-related anxiety was developed in
1990 and was therefore based on technological devices,
including the videocassette recorder and microwave oven,
considered modern in that period. Technology-related anxi-
ety today may best be understood in the context of interac-
tive devices such as the iPad and smart phones. These
devices are exponentially more complicated and serve more
purposes than technology from the 1990s, and so technol-
ogy-related anxiety in 1990 was likely a somewhat different
construct to technology-related anxiety today. As this is the
first study to find a strong relationship between technology-
related anxiety and positive Attitudes Toward Technology,
it is important for future research to explore this further.
Future research on the use of iPads would also benefit from
the development of scales specifically focused on this
modality, rather than the use and adaptation of scales based
on ICT generally.

The strengths of this study are that it provides insights
into the complex interactions between anxiety, self-efficacy,
attitudes, and behavior toward the use of ICT-based
resources in parents of children with ASD and professionals
who work with these children. There are some limitations
that need to be taken into account when interpreting the
results of this study. This study attempted to gauge an
aggregated use of the iPad by the child with ASD over a
5-day period, as reported by parents. However, it is evident
that providing an accurate measure of time spent using the
device is difficult. Data were not collected regarding the
type of applications that were used by children at home or
parental views on the purpose of the applications being used
(i.e., were they seen as beneficial for educational or skill
development or for other purposes such as entertainment).
Furthermore, relying on self-report of behavior is problem-
atic, as the information obtained is subjective and retrospec-
tive. Perhaps future research could attempt to develop an
electronic mechanism, such as an iPad application, that
accurately measures iPad use by children. Validated
research in this area may help clarify current usage norms in
this population.

Although this study provides insight into the attitudes of
parents and professionals toward iPad application use in
ASD, further research is required to provide scientific evi-
dence for the effects of iPad use. It is important to acknowl-
edge that there is likely to be differential efficacy across
iPad applications depending on the applications themselves,

user variables such as child’s skill level and severity of
ASD. Thus, several factors should be considered when
assessing the use of iPad applications for children with
ASD. The rapidly increasing use of iPads among this popu-
lation, and the potential they hold as a means for delivering
time- and cost-effective, education and goal-based inter-
vention to children with ASD, suggests that the research
work should progress as a matter of some urgency.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this


The author(s) received no financial support for the research,
authorship, and/or publication of this article.


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lable at ScienceDirect

Journal of Psychiatric Research 90 (2017) 1e11

Contents lists avai

Journal of Psychiatric Research

journal homepage: www.elsevier.com/locate/psychires

Improving therapeutic outcomes in autism spectrum disorders:
Enhancing social communication and sensory processing through the
use of interactive robots

Felippe Sartorato a, Leon Przybylowski a, Diana K. Sarko b, c, *

a Osteopathic Medical Student (OMS-IV), Edward Via College of Osteopathic Medicine (VCOM), Spartanburg, SC, USA
b Department of Anatomy, Southern Illinois University School of Medicine, Carbondale, IL, USA
c Department of Psychology, Southern Illinois University School of Medicine, Carbondale, IL, USA

a r t i c l e i n f o

Article history:
Received 16 November 2016
Accepted 3 February 2017

Social robot
Socially assistive robot (SAR)

* Corresponding author. 1135 Lincoln Drive, Southe
Carbondale, IL 62901, USA.

E-mail address: dsarko38@siumed.edu (D.K. Sarko


© 2017 Elsevier Ltd. All rights reserved.

a b s t r a c t

For children with autism spectrum disorders (ASDs), social robots are increasingly utilized as therapeutic
tools in order to enhance social skills and communication. Robots have been shown to generate a number
of social and behavioral benefits in children with ASD including heightened engagement, increased
attention, and decreased social anxiety. Although social robots appear to be effective social reinforce-
ment tools in assistive therapies, the perceptual mechanism underlying these benefits remains unknown.
To date, social robot studies have primarily relied on expertise in fields such as engineering and clinical
psychology, with measures of social robot efficacy principally limited to qualitative observational as-
sessments of children’s interactions with robots. In this review, we examine a range of socially interactive
robots that currently have the most widespread use as well as the utility of these robots and their
therapeutic effects. In addition, given that social interactions rely on audiovisual communication, we
discuss how enhanced sensory processing and integration of robotic social cues may underlie the
perceptual and behavioral benefits that social robots confer. Although overall multisensory processing
(including audiovisual integration) is impaired in individuals with ASD, social robot interactions may
provide therapeutic benefits by allowing audiovisual social cues to be experienced through a simplified
version of a human interaction. By applying systems neuroscience tools to identify, analyze, and extend
the multisensory perceptual substrates that may underlie the therapeutic benefits of social robots, future
studies have the potential to strengthen the clinical utility of social robots for individuals with ASD.

© 2017 Elsevier Ltd. All rights reserved.


1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Sensory perception & integration: deficits & therapeutic targets in ASD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.1. Integration of multiple sensory modalities: challenges and opportunities in ASD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.2. Assessing perceptual integration of multisensory cues: the temporal binding window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3. The neurobiology of social interaction & communication deficits in ASD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1. The neurobiology of perception of social robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

4. A spectrum of socially-assistive robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4.1. Humanoid robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4.2. Cartoonish robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4.3. An unsuccessful robot model: avoiding the “uncanny valley” of social robotic design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
4.4. Social robots in animal form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4.5. Social robots in robotic form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

rn Illinois University,









F. Sartorato et al. / Journal of Psychiatric Research 90 (2017) 1e112

5. Towards an optimal robotic model for use in ASD therapies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
6. Applying systems neuroscience tools to strengthen and extend social robot therapeutic value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
7. Access to social robot therapies and methodological considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1. Introduction

Autism spectrum disorders (ASD) include a continuum of defi-
cits characterized to varying extents by difficulties with commu-
nication and social interactions, repetitive behaviors, and restricted
interests (American Psychiatric Association, 2013; Kanner, 1943;
Lord et al., 2000). Social deficits may include a variety of impair-
ments during interactions, including difficulty initiating joint
attention behaviors and responding to joint attention tasks
(Charman et al., 1997; Leekam et al., 1997; Mundy and Sigman,
1989). Children with ASD often exhibit a diminished ability to
imitate others (Ingersoll, 2008; Rogers and Pennington, 1991;
Williams et al., 2004), which is critical due to the key role that
imitation is thought to play in the development of social cognition
(Meltzoff and Decety, 2003). In addition, individuals with ASD
frequently exhibit reduced gaze fixation (Baron-Cohen et al., 2000;
Dalton et al., 2005; Lord et al., 2000) and a reduced ability to
recognize and respond appropriately to emotional expressions
(Celani et al., 1999), making social interactions frustrating,
confusing, and potentially aversive. The prevalence of ASD appears
to be increasing, with recent estimates as high as 1 in 68 (Baio,
2014). Since few treatment options currently exist, there is a crit-
ical need for establishing novel, effective support tools and thera-
peutic intervention strategies.

Social robots were recently discovered to be promising tools in
the diagnosis and treatment of ASD, particularly due to the fact that
individuals with ASD often show an interest in technology
(Dautenhahn and Werry, 2004; Diehl et al., 2012, 2014b; Feil-Seifer
and Mataric, 2009; Scassellati, 2007). Robots appear to be more
effective than interactive software or computer-mediated therapy
based on their flexible capacity for interactive play and engaging
multisensory design features, including realistic 3-dimensional
body movements (Cabibihan et al., 2013; Kim et al., 2013). Both
adults and children (typically developing or otherwise) have a
natural inclination to anthropomorphize life-like robots, attrib-
uting human-like motivations and intentions to robots and relying
on human social rules when interacting with them (Hinds et al.,
2004; Reeves and Nass, 1996). In fact, typically developing tod-
dlers have been shown to treat robots as peers rather than as toys
following repeated exposures to, and interactions with, the robots
(Tanaka et al., 2007). If our human social rules and interactions can
be generalized to interactions with robots, social robots may
represent an ideal tool for facilitating the development of social
skills and for delivering interventions that alleviate social diffi-
culties for individuals with ASD. Robotic interactions are inherently
more controlled, predictable, and simplistic, thereby generating
less frustration for individuals with ASD who may have difficulty
interpreting and responding to human social interactions. Children
with ASD are proactive in initiating interactions with social robots
(Dautenhahn, 2007); produce more speech overall in the presence
of a social robot (Kim et al., 2013); and direct more speech (social
interaction) toward adults in the same room when also in the
presence of a social robot (Kim et al., 2013). In addition, robots are
effective at attracting gaze (Werry et al., 2001), and interactions
with robots have been shown to significantly decrease social anx-
iety in children with ASD (Kaboski et al., 2015). Developing early

social and communication intervention strategies targeted towards
children with ASD is of particular interest because children are
especially vulnerable to increasingly complex social demands as
they transition to adulthood (Webb et al., 2004). Delays in the
development of age-appropriate social communication can be
highly detrimental to individuals with ASD, leading to increased
social anxiety and depression as well as diminished occupational/
professional success as adults (Gillott et al., 2001; Sterling et al.,

2. Sensory perception & integration: deficits & therapeutic
targets in ASD

The development of social robots as a therapeutic tool for in-
dividuals with ASD has benefited extensively from advances in
engineering and adoption by clinical psychologists. Despite the fact
that social interactions inherently rely on audiovisual communi-
cation, systems neuroscience approaches addressing the mecha-
nism and efficacy of the therapeutic utility of social robots remain
largely unexplored. Systems neuroscience analyses offer the op-
portunity to elucidate critical components of the perception of, and
social interactions with, robots for individuals with ASD. This in
turn will allow optimization of the sensory cues delivered by robots
to generate the greatest degree of behavioral benefits possible.

Altered sensory perception is an integral part of ASD sympto-
mology, with observations of sensory disturbances dating back to
Kanner’s first observations (Kanner, 1943). Individuals with ASD
often demonstrate a range of reactions to sensory stimuli that are
not found in typically developing individuals, including both
overstimulation aversions and hyposensitivity (Baranek et al.,
2006; Cascio et al., 2015; Grandin, 2000; Kientz and Dunn, 1997;
Leekam et al., 2007; O’Neill and Jones, 1997; Puts et al., 2014;
Rogers et al., 2003; Talay-Ongan and Wood, 2000; Tavassoli et al.,
2016). Although deficits in sensory processing are observed in ASD,
enhanced processing of certain sensory cues, particularly “simple”
stimuli (e.g., LED flashes and pure tones), have also been observed
(Bonnel et al., 2003; Cascio et al., 2008; Mottron et al., 2006;
O’Riordan and Passetti, 2006). The deficiencies observed in
perceptual processing in ASD are commonly related to more com-
plex stimuli (e.g., emotional facial expressions, speech) (Bertone
et al., 2005; Boddaert et al., 2004a; Minshew and Hobson, 2008).
Visual perception of biological motion is also impaired (Bertone
et al., 2003; Blake et al., 2003; Kaiser and Shiffrar, 2009; Spencer
et al., 2000) with higher visual motion coherence thresholds
(Spencer et al., 2000) and deficits in perception of complex and
biological motion (Bertone et al., 2003; Blake et al., 2003).

2.1. Integration of multiple sensory modalities: challenges and
opportunities in ASD

Beyond perception of a single modality stimulus alone (e.g.,
visual alone, such as a flash of light from an LED), impairments
related to combining signals from multiple sensory modalities
(known as multisensory integration) are present in individuals
with ASD (e.g., (Donohue et al., 2012; Stevenson et al., 2014b)).
Information from multiple sensory modalities must be combined in

F. Sartorato et al. / Journal of Psychiatric Research 90 (2017) 1e11 3

a meaningful way and filtered accurately in order to derive
perceptual meaning from our surroundings and respond with
appropriate behavior. Multisensory integration is detectable at the
neural level (for instance, as a significant increase in neural
response/firing rate when an LED and white noise burst are pre-
sented together, compared to an LED presented alone or a noise
burst presented alone) and at the behavioral level (as faster reac-
tion times, improved target detection, enhanced orientation, and
increased accuracy in response to multisensory stimuli) (see (Sarko
et al., 2013) for review). With its critical role in shaping normal
perceptual processes, impairments in effective multisensory inte-
gration can generate a profoundly altered sensory environment
that is hyperstimulating and overwhelming, characterized by
improper filtering of signal vs. noise and binding of multisensory
signals that should not be perceptually bound. Emerging evidence
suggests that altered multisensory processing may play a contrib-
utory role in the etiology of ASD (Ciesielski et al., 1995; Foss-Feig
et al., 2010; Kern, 2002; Kwakye et al., 2011; Stevenson et al.,
2014b). The structure and function of multisensory brain networks
also appear to be compromised in individuals with ASD (Boddaert
et al., 2004b; Zilbovicius et al., 2006). In addition, new evidence
emphasizes atypical multisensory integration as a contributing
factor in the social and linguistic difficulties typically seen in those
suffering from ASD (Senkowski et al., 2008).

2.2. Assessing perceptual integration of multisensory cues: the
temporal binding window

The behavioral benefits of effective multisensory integration are
derived from underlying neural operations that result from the
convergence and integration of inputs from multiple sensory mo-
dalities. In order to determine which information from different
senses should be perceptually bound (encoded as belonging to a
common source), the brain relies on certain statistical regularities
of sensory stimuli, such as the speed of light vs. sound and the
relative neural conduction speeds of each sensory modality. Due to
differing propagation speeds, the nervous system allows a certain
degree of temporal offset (typically ~300ms) in which multisensory
stimuli will be perceptually and neurophysiologically bound
together and perceived as a coming from a common source
(Meredith et al., 1987; Shams et al., 2002). Thus, the timeframe in
which multisensory stimuli are highly likely to be integrated and
perceived as simultaneous (i.e., belonging to a common source) is
known as the “temporal binding window.” This has become a useful
construct in assessing an individual’s capacity to filter environ-
mental stimuli appropriately, determining stimuli that belong

Fig. 1. Adults with ASD exhibit wider temporal binding windows (TBWs) for more comp
permission). A) The temporal binding window is assessed as the width (in ms) at 75% of max
stimuli resulted in significantly wider temporal binding window size in individuals with A
(auditory cue precedes visual cue), TBW ¼ temporal binding window, TD ¼ typically devel

together (to a common source) versus those that do not (disparate
sources) (e.g., (Foss-Feig et al., 2010; Stevenson et al., 2014a,
2014b)). A study by Foss-Feig et al. demonstrated that for chil-
dren with ASD, the temporal binding window was approximately
twice as large compared to that of typically developing children in
response to certain multisensory illusion stimuli (Foss-Feig et al.,
2010). Other studies have shown that the most profound effects
of temporal binding window enlargement in adults with ASD are
present for speech stimuli (Stevenson et al., 2014b) (Fig. 1). Func-
tionally, this diminished ability to accurately filter environmental
stimuli has a profound impact on successfully navigating the world
around us.

Larger temporal binding windows in individuals with ASD may
contribute toward the perceptual substrates underlying many
pervasive characteristics put forward in the Intense World Theory
(Markram et al., 2007; Markram and Markram, 2010). This theory
proposes that the hypersensitivity and “sensory overload” that in-
dividuals with ASD experience may be due to neuropathology of
hyper-active neuronal circuits. By combining multisensory infor-
mation that should normally be separated, enlarged, more inclusive
temporal binding windows may create an improperly filtered – and
therefore more confusing and overwhelming – perceptual world.
This in turn may alter the processing of sensory stimuli that would
normally be perceived as innocuous (e.g., visits to a movie theater,
social encounters) to instead be perceived as acutely unpredictable,
aversive, anxiety-inducing, and potentially unbearable.

3. The neurobiology of social interaction & communication
deficits in ASD

The inherent heterogeneity of autism spectrum disorders leads
to difficulty in pinpointing common neural substrates that may
cause core deficits. However, certain neurobiological irregularities
do appear to be overarchingly characteristic of ASD. Several neural
mechanisms have been proposed to underlie the core social and
communication deficits observed in ASD. One such network in-
volves the mirror neuron system, which facilitates imitation and
social communication. In a fMRI experiment studying imitation and
observation of emotional expressions, children with ASD exhibited
low levels of activity in the mirror neuron regions of the inferior
frontal gyrus, pars opercularis, indicating that mirror neuron sys-
tem dysfunction may contribute to core social deficits in ASD
(Dapretto et al., 2006). Impairments in accurate recognition of
emotional facial expressions and emotion communicated through
body language, particularly with respect to fear, may be related to
abnormalities of the amygdala in individuals with ASD.

lex human speech stimuli ((Stevenson et al., 2014b), Copyright 2014, reprinted with
imum simultaneity response in a simultaneity judgement task. B) Only complex speech
SD compared to TD individuals. ASD ¼ autism spectrum disorders, AV ¼ Audio-visual
oped, VA ¼ Visual-audio (visual cue precedes audio).

F. Sartorato et al. / Journal of Psychiatric Research 90 (2017) 1e114

Neuroimaging and neuropathology studies suggest that amygdala
dysfunction may underlie social cognition deficits (Abell et al.,
1999; Adolphs et al., 2001; Aylward et al., 1999; Baron-Cohen
et al., 1999; Bauman and Kemper, 1985; Hadjikhani et al., 2009;
Howard et al., 2000; Nacewicz et al., 2006; Pierce et al., 2001;
Schultz, 2005; Schumann and Amaral, 2006) and gaze aversion
(Spezio et al., 2007).

Individuals with ASD exhibit weak or absent activation in the
fusiform gyrus, a brain region involved in face recognition, as well
as reduced activation of the inferior occipital gyrus, superior tem-
poral sulcus, and amygdala during human face perception tasks
(Pierce et al., 2001). Altered functional connectivity has also been
demonstrated during human face processing in individuals with
ASD, including reduced connectivity between the fusiform face
area and the amygdala that correlated with the degree of social
impairment (Kleinhans et al., 2008). One study found that instead
of activating brain areas typically involved in human face percep-
tion, individuals with ASD activated an aberrant and heterogeneous
mix of other brain regions (Pierce et al., 2001). This indicates that
these individuals perceive faces by recruiting altered neural net-
works. Interestingly, a fMRI case study of a boy with ASD who was
particularly interested in Digimon cartoon characters revealed that
these cartoons activated typical face processing regions (fusiform
gyrus and amygdala) but that human faces did not (Grelotti et al.,
2005). These findings reveal that the neural circuitry critical to
social interactions – specifically, involving face perception – can be
recruited by neural networks typically devoted to human faces. The
fusiform gyrus also responds to non-facial objects related to visual
expertise, possibly subserving some of the restricted interests
characterizing individuals with ASD (Foss-Feig et al., 2016). Such
neurobiological studies in ASD indicate that the neural circuitry of
social interactions is malleable – and potentially highly targetable –
for therapeutic interventions through the use of other simplified/
non-human faces such as those of social robots.

3.1. The neurobiology of perception of social robots

If robots are to act as therapeutic interventions and social fa-
cilitators, it is important to elucidate how robots are perceived,
both at behavioral and neural levels. In a recent fMRI study, typi-
cally developed adults and those diagnosed with ASD played an
interactive game of rock, paper, scissors against 3 different oppo-
nents: a person, a humanoid robot (Bioloid), or a random number
generator (Chaminade et al., 2012). Typically developed adults
exhibited increased activity in pSTG (posterior superior temporal
gyrus, involved in social cognition and multisensory integration)
when interacting with a person compared to the robotic opponent.
This indicates that typically developed adults were able to effec-
tively differentiate between humans and robots. Adults with ASD
did not demonstrate this differentiation, instead exhibiting com-
parable activity in pSTG against both the human and robotic op-
ponents. This could be interpreted as an inability to discern the
intentionality of the human vs. the robot (Chaminade et al., 2012)
or as a reduced ability to perceive the “humanness” of the human
opponent (Kuriki et al., 2016). Another potential explanation for
reduced pSTG activity in individuals with ASD when confronted
with human opponents could be due to altered perception of
multisensory cues compared to typically developed individuals.
Regardless of which theory (or combination thereof) is correct,
there is high therapeutic potential in exploiting the fact that in-
dividuals with ASD may perceive robots as interchangeable,
acceptable, and perhaps preferable social interaction partners.

4. A spectrum of socially-assistive robots

The use of robots for individuals with ASD is a relatively novel
therapeutic tool gaining traction over the last decade (Aresti-
Bartolome and Garcia-Zapirain, 2014; Coeckelbergh et al., 2016).
During that time, researchers and clinicians have developed robotic
models with a wide range of appearances, features, and functional
capabilities that draw from expertise in fields such as engineering
and clinical psychology (Scassellati et al., 2012). Robotic interactive
features include various degrees of capability with respect to bio-
logical motion (e.g., walking, jumping, or dancing), body language
(e.g., shrugging shoulders; tilting, turning, or shaking head), gaze
direction to indicate attention, facial expression (e.g., smiling or
frowning, lip/eyebrow/eyelid/ear movement), and vocalization
(with varying levels of emotional prosody, from more robotic to
more human-like speech) (Cabibihan et al., 2013; Pennisi et al.,
2016). Such features are varied in order to optimally target and
alleviate the deficits associated with ASD (Fig. 2), particularly with
respect to social interactions and communication. Below, we will
examine a range of interactive robot designs that have been used to
target ASD; the therapeutic effectiveness of these robots in working
with individuals with ASD; which features of robotic appearance
and capability appear to generate optimal clinical utility; and ulti-
mately, how systems neuroscience tools may be used to further
extend treatment of social and communication symptomology in
children with ASD through the use of social robots.

4.1. Humanoid robots

In some cases, robotic designs rely on human-like appearances
to varying degrees in order to increase realism and evoke engage-
ment and interaction. The AuRoRA (Autonomous mobile Robot as a
Remedial tool for Autistic children) project utilized robots to
engage children with ASD in play behavior and social interactions
(Dautenhahn, 1999). A robotic doll named Robota (Fig. 2A) with a
human appearance was capable of limited arm, leg, and head
movement; reaction to touch through the use of potentiometers to
detect passive deflection; and robotic vocalizations through speech
synthesizing. Robota was successfully used to encourage imitation
of movements, social interaction, joint attention, and play in chil-
dren with ASD (Billard et al., 2007; Dautenhahn and Billard, 2002;
Robins et al., 2005).

A different humanoid robot model named KASPAR (Kinesics And
Synchronization in Personal Assistant Robotics; Fig. 2B) was
developed to look like a 3-year-old boy (Dautenhahn et al., 2009).
KASPAR had the ability to move its arms and head, to blink and
make limited facial expressions (e.g., smiling), and to vocalize using
a neutral male human voice. After playing a video game with
KASPAR, children with ASD exhibited increased collaborative play
with human adults, indicating that KASPAR was effective at
enhancing social behaviors and generalized social engagement
(Wainer et al., 2010).

4.2. Cartoonish robots

Alternative robotic models deviate from a human appearance in
favor of designs with the appearance of approachable, cartoonish
creatures. A study by Duquette et al. (2008) used a robotic design
named Tito with a cartoonish appearance. Tito was capable of some
degree of mobility (moving on wheels), arm movement, head
movement, facial expressions related to a series of LEDs depicting a
mouth, and vocalizations emitted as pre-recorded speech (a male
voice that could be neutral, happy, or interrogative). Compared to
human interactions, interactions with Tito generated more shared
attention (gaze directed towards Tito, physical proximity) and

Fig. 2. Examples of robots with a variety of capabilities and appearances used in therapeutic applications for autism spectrum disorders. These robots range from appearances that
are human-like (Robota, A; KASPAR, B) (images reprinted with permission from (Dautenhahn et al., 2009)); cartoonish (Keepon, C) (image reprinted with permission from (Costescu
et al., 2015)); animal-like (PABI, E; Probo, F; and Pleo, G) (images reprinted with permission from (Dickstein-Fischer and Fischer, 2014); (Goris et al., 2010), originally published by
INTECH as an Open Access work, http://dx.doi.org/10.5772/8129; and (Kim et al., 2013), respectively); and robotic with human traits (NAO, H) (image reprinted with permission
from (Shamsuddin et al., 2012)). Also note the robotic model Infanoid (D), a precursor to Keepon (C) that was considered too overwhelming and generated reactions of anxiety and
embarrassment in children with ASD (image reprinted with permission from (Kozima et al., 2007)).

F. Sartorato et al. / Journal of Psychiatric Research 90 (2017) 1e11 5

imitation of facial expressions in children with ASD (Duquette et al.,
2008). Thus, Tito was successful in eliciting social interactions in
low-functioning young children with ASD (Duquette et al., 2008).

Keepon, an interactive robot with a cartoonish, snowman-like
appearance (Fig. 2C) has been shown to successfully engage eye
contact and facilitate joint attention (Kozima et al., 2007, 2009). A
longitudinal study conducted at a day care center over the course of
4 years found that young children with ASD were motivated to
interact with Keepon, and that Keepon could effectively engage
their attention (Kozima et al., 2007, 2009). The children exhibited
caretaking behavior by placing clothing on the robot and feeding it
toy food. Keepon also elicited emotional reactions from the children
(e.g., surprise and joy). Furthermore, the children were motivated
to share the interests and emotions resulting from the robot
interaction with a human adult, thus increasing human interactions
as a result of robot interactions. The authors concluded that
Keepon’s more simplistic forms of expression (limited to atten-
tional direction of head and eyes, and body movements to express
emotions such as pleasure, excitement, or fear by rocking side to
side, bouncing up and down, or vibrating, respectively e no vo-
calizations) allowed the children to detect and respond to socially
meaningful cues (Kozima et al., 2007, 2009). In another study,
Keepon was used in a reversal learning task to assess cognitive
flexibility in children with ASD (Costescu et al., 2015). The task
required the children to flexibly adapt their behavioral responses to
changing environmental rules. Although the children’s cognitive
flexibility performance was similar during robot interactions and
human interactions, the children exhibited more attentional
engagement in the reversal learning task and appeared to enjoy the
task more (exhibited positive affect more frequently) during in-
teractions with Keepon (Costescu et al., 2015).

4.3. An unsuccessful robot model: avoiding the “uncanny valley” of
social robotic design

Interestingly, a precursor to the Keepon robot named Infanoid
was abandoned in favor of the more simplistic Keepon model.
Infanoid’s body was an upper torso model, approximately the size
of a 4-year-old child, with exposed robotic machinery (Fig. 2D).
Infanoid had a much higher degree of complexity with 29 actuators
(compared to Keepon’s 4) controlling a large degree of movement
capability to the arms, face, lips, eyebrows, and ears. This high
degree of expressivity appeared to generate an overwhelming
amount of stimulation, particularly for young children ages 3 and
under with ASD who exhibited anxiety and embarrassment upon
first being presented with Infanoid (Kozima et al., 2007, 2009). The
authors attributed this reaction to the anthropomorphic but highly
mechanistic appearance of Infanoid, as well as the overwhelming
number of moving parts providing distracting information that was
difficult for the children to integrate into a meaningful, holistic
social interaction (Kozima et al., 2007, 2009).

The shortcomings of Infanoid are highly informative for at least
two reasons. First, they speak to the importance of the degree to
which a robot has an approachable, human-like appearance. Infa-
noid may have fallen into the “uncanny valley” of robotic design
(Mori, 1970). This concept describes the fact that people find robots
to be more engaging and approachable as the robots become more
realistic, but only up to a point. As a robot approaches a stage just
short of perfect realism, its appearance becomes disturbing. For
robots with these design features the acceptance of, and comfort
level with, the robot plummets (the “uncanny valley”), and the robot
instead becomes aversively “creepy” (MacDorman et al., 2009).
Neural correlates to the uncanny valley phenomenon have been


F. Sartorato et al. / Journal of Psychiatric Research 90 (2017) 1e116

suggested in typically developed adults, particularly with respect to
mismatch between the appearance and movements of the robot
generating prediction errors that ultimately result in the perception
that the robot is aversively “not quite right” (Saygin et al., 2012).
Avoiding the uncanny valley is particularly important in tailoring
therapies for children with ASD, for whom social interactions can be
inherently off-putting (and, interestingly, the uncanny valley may be
significantly altered in ASD – see (Ueyama, 2015)).

The second informative aspect of Infanoid’s shortcomings was
that certain design features may be overwhelming for individuals
with ASD during human social interactions. Human interactions
involve high degrees of complexity, numbers of moving parts, and
levels of expression – features also attributable to Infanoid and were
found to be detrimental – that can form an overwhelming flood of
social and sensory cues for individuals with ASD. The key to
interactive robots’ therapeutic value as social facilitators may lie in
allowing individuals with ASD to experience social cues through a
simplified and predictable version of a human interaction. This
creates a more perceptually palatable experience that may opti-
mize social learning that can be applied to goal-oriented treat-
ments and can then be generalized to facilitate human interactions.

4.4. Social robots in animal form

Numerous studies have also examined the utility of social robots
in animal form when developing therapies for children with ASD.
PABI (Penguin for Autism Behavioral Intervention; Fig. 2E) was
recently designed as an inexpensive robot that could be made
readily available to families of children with ASD as a therapy tool
(Dickstein-Fischer et al., 2011). PABI was able to mimic human
emotions while maintaining a simplistic, approachable form. With
11 degrees of freedom, PABI could move its head, eyes, eyelids,
beak, and wings. PABI was further able to vocalize through pre-
recorded sounds and was equipped with face-tracking capability.
PABI has been proposed for use as an early-intervention tool for
diagnosis, particularly with respect to measurement of gaze di-
rection and social gestures (Dickstein-Fischer and Fischer, 2014).
This robot can also be effectively utilized in robot-assisted applied
behavior analysis (ABA), a commonly used therapeutic approach in
ASD that applies principles of learning and motivation to reduce
interfering behaviors, teach new skills, and increase targeted pos-
itive behaviors (e.g., self-control, social interaction) (Dickstein-
Fischer and Fischer, 2014).

In a recent study by Vanderborght et al. (2012), a social robot
named Probo, which has the appearance of a stuffed elephant toy
(Fig. 2F), was used to tell “Social Stories” to children with ASD.
These stories depicted scenarios aimed at helping children to better
understand social situations and to generate appropriate social
behavior (e.g., “How to Say Hello” teaches the child to say hello
when s/he meets someone) (Gray, 2000). Probo was able to direct
gaze, generate facial expressions (including eye blinking, ear flap-
ping, mouth movement, head nodding/shaking), and deliver pre-
recorded vocalizations (a neutral male voice) (Goris et al., 2010).
The social performance of children with ASD was found to improve
when Probo, rather than a human reader, told the “Social Stories”
(Vanderborght et al., 2012), indicating added therapeutic value and
facilitated social learning. Probo has also been used to examine the
use of social robots during play tasks. During a particular play task
(making a fruit salad), children with ASD directed more eye contact
towards Probo compared to a human counterpart, although other
social interaction measures did not significantly differ (Simut et al.,
2016). The use of Probo likely creates a more easily understandable
social context in which children can learn social skills through
repeated tasks with consistent, controlled, predictable stimuli and

Additional robotic designs have focused on other animal forms.
Kim et al. (2013) studied the interactions of children with a social
robot dinosaur compared to interactions with a human adult or an
asocial technology (touchscreen computer game). The dinosaur,
Pleo (Fig. 2G), was capable of walking, moving its tail and head
expressively, blinking, moving its mouth, and pseudo-vocalizing
(e.g., “Heee!” for greeting/satisfaction, “Unh uhn” for “no”). The
study found that children with ASD ages 4 to 12 produced more
speech overall, and directed more speech toward a human adult in
the same room, when interacting with Pleo compared to interact-
ing with a person or an asocial technology (Kim et al., 2013). Thus,
animal forms of social robots appear to also serve as effective social
facilitators that may be used in social and communication

4.5. Social robots in robotic form

Recent studies have examined social interactions between
children with ASD and a mini-humanoid robot named Nao
(Aldebaran Robotics; Fig. 2H). Nao is capable of 25 degrees of
freedom allowing it the ability to walk, generate human-like body
language, and move its head. Nao is equipped with touch sensors as
well as 2 cameras, one located at its mouth, rendering it incapable
of lip or jaw movement during speech. However, Nao is capable of
emitting vocalizations, either using its standard robotic voice –
which is capable of some prosodic inflection – or a pre-recorded
human voice. In addition, each of Nao’s eyes is made up of 8 LEDs
which can be illuminated in different colors or dimmed differen-
tially to produce the appearance of blinking. Nao’s “simple” façade
is thought to reduce confusion and overstimulation in children with
ASD (Huskens et al., 2013). Compared to typically developing
children, children with ASD spent significantly more time looking
at Nao, and the robot was able to successfully facilitate joint
attention behaviors (Bekele et al., 2013). In one particular case
study of a child with ASD, the boy avoided a human adult’s gaze but
made eye contact readily and easily with Nao, particularly if Nao’s
eyes changed color, or if Nao spoke or moved (Shamsuddin et al.,
2012). Compared to interactions with other people in a classroom
setting, interactions with Nao also improved the boy’s repetitive
behaviors, communication, and social interactions (Shamsuddin
et al., 2012). In a different study comparing delivery of ABA inter-
vention, Nao was found to be as effective as a person in training
children with ASD to self-initiate question-asking (Huskens et al.,
2013). Interactions with Nao have been shown to improve body
coordination, imitation/praxis (ability to plan and execute gestures
and actions), and interpersonal synchrony (coordinating actions
with those of another, which requires turn-taking, attention, and
imitation skills) (Srinivasan et al., 2015). In addition, Nao has been
used in a pilot study robotics camp for typically developing (TD)
adolescents and those with ASD (Kaboski et al., 2015). The ado-
lescents attending the robotics camp learned to cooperatively
program Nao robots to perform a social interaction with a crowd (1
ASD: 1 TD per programming pair, with supervising facilitators). This
camp experience increased robotics knowledge, teaching the ado-
lescents a potential vocational skill, and was also shown to signif-
icantly reduce social anxiety in adolescents with ASD by the end of
the week-long camp (Kaboski et al., 2015).

5. Towards an optimal robotic model for use in ASD therapies

There are a variety of outcome measures of robot-assisted
therapy such as 1) generalization to human interactions; 2)
increased cooperation/collaboration; 3) reduction of repetitive
behaviors and restricted interests; 4) increased sharing and turn-
taking behaviors; 5) enhanced imitation or joint attention

F. Sartorato et al. / Journal of Psychiatric Research 90 (2017) 1e11 7

capabilities; and 6) increased motivation and attentional engage-
ment. These outcomes vary according to the traits of the robot
being used as well as the severity of symptoms for individuals with
ASD (Cabibihan et al., 2013; Costa et al., 2010; Dautenhahn, 2007;
Diehl et al., 2012, 2014a; Goodrich et al., 2012; Jordan et al., 2013;
Pierno et al., 2008). In an assessment of outcomes using different
robot-assisted therapy models for ASD, humanoid robots were
found to elicit enhanced generalization of skill sets taught during
therapy sessions (e.g., turn-taking, sharing) (Ricks and Colton,
2010). Human-like features such as the presence of facial expres-
sions, vocalizations, and moving limbs have been shown to engage
attention, increase interaction, and improve utilization of intent-
matching facial expressions (Lee et al., 2012). However, non-
humanoid robots elicited the most attentional engagement (Ricks
and Colton, 2010). Social robots with soft edges and coloration
that is bright enough to attract attention, but not too bright to the
point of overstimulation, have also been found to strike an ideal
appearance (Hoa and Cabibihan, 2012; Michaud et al., 2003). Ro-
bots with a simple appearance increase levels of interaction and are
more readily accepted by children with ASD (Robins et al., 2006). In
addition to these features, the gender of both the robot and the
individual for whom the social robot therapy is targeted should be
considered as important factors when creating individualized
therapy plans and optimizing the impact and success of the therapy
(Laue, 2015). For instance, typically developed adults were found to
rate a robot of the opposite sex to be more trustworthy, engaging,
and credible than a robot matching the gender of the individual
(Siegel et al., 2009). Emerging evidence suggests a relatively high
co-occurrence between gender dysphoria and ASD (Glidden et al.,
2016; Van Der Miesen et al., 2016), making it all the more critical
to be accurately attuned to the individual’s gender identity when
developing an optimal therapeutic plan incorporating social robots.
Thus, the perceived gender of the robot, as well as the gender
identity of the individual with ASD, must be taken into careful
consideration for targeted, individualized social therapy strategies
in order to facilitate interactions and maximize beneficial effects.

6. Applying systems neuroscience tools to strengthen and
extend social robot therapeutic value

Although social robots appear to be effective therapy tools, the
perceptual mechanisms underlying these benefits remain largely
unknown. Given that social interactions rely on audiovisual
communication, it seems likely that social robot stimuli confer
added multisensory processing benefits that are lacking in human
interactions. These benefits may rely on social robots acting as
simplified versions of people, allowing more effective filtering of
meaningful perceptual stimuli. Recent studies in our lab have
begun to address this question by applying systems neuroscience
tools to target the multisensory perceptual substrates that may
underlie the therapeutic benefits of robots with respect to social
skills and communication. Human stimuli may confer helpful social
cues (e.g., body language, emotional prosody of speech) to typically
developing individuals, which would facilitate enhanced process-
ing and result in narrower temporal binding windows for human
stimuli compared to robotic stimuli. Conversely, children with ASD
may exhibit enhanced processing (as indicated by narrower tem-
poral binding windows) for social robotic stimuli compared to more
complex, potentially confusing or overwhelming human stimuli. A
better understanding of the perceptual substrates underlying the
therapeutic benefits of social robots, utilizing systems neuroscience
tools, would allow the development of social & behavioral thera-
pies that extend these benefits and are better targeted to alleviate
perceptual, social, and communication difficulties for individuals
with ASD.

This could be accomplished in a variety of ways, but one such
promising approach is to first identify the potential perceptual
benefits conferred by social robots through a simultaneity judge-
ment (SJ) task to analyze temporal binding window size. In the SJ
task, participants can be shown short videos of robots or humans in
which the auditory and visual components of the stimulus occur at
the exact same time (synchronous/simultaneous) or are temporally
offset by up to 400 ms with either the auditory or the visual
component occurring first (Fig. 3). The participant is asked to report
whether the auditory and visual components of each video
occurred at the same time or at different times. As would be ex-
pected, participants report a high degree of simultaneity when the
stimuli are in fact synchronous, and report a low degree of simul-
taneity for large degrees of temporal offset (e.g., auditory cue pre-
ceding visual cue by 400 ms, or vice versa). The temporal binding
window is then quantified as the width (in ms) of this approxi-
mately bell-shaped curve at 75% of the maximum simultaneity
response for each individual (e.g., (Stevenson et al., 2014a)). This SJ
task paradigm has been successfully employed using a range of
multisensory stimuli from simplistic (a flash of light paired with a
beep sound) to complex (speech involving body/face movement
and vocalization) (Stevenson and Wallace, 2013). Temporal binding
window size analysis has been used in a wide age range of partic-
ipants, from children to elderly adults (Bedard and Barnett-Cowan,
2016; Hillock-Dunn and Wallace, 2012; Hillock et al., 2011; Lew-
kowicz and Flom, 2014). It has also been successfully directed to-
ward elucidating sensory integration impairments in a number of
different clinical conditions (Wallace and Stevenson, 2014),
including schizophrenia (Foucher et al., 2007), dyslexia (Hairston
et al., 2005), and ASD (Stevenson et al., 2014b).

Although such analyses have characterized multisensory inte-
gration using a variety of stimuli (Stevenson and Wallace, 2013),
they have not yet been directed toward characterizing the
perception of multisensory social cues from interactive robots.
Using videos of social interactions from a social robot, and
comparing temporal binding window size for robotic social stimuli
compared to videos of comparable human social stimuli, is one
method that has great potential for elucidating the perceptual
substrates underlying the therapeutic utility of social robots. This
methodology relies on systems neuroscience tools as a novel
approach towards identifying the multisensory processing benefits
that social robots may confer to individuals with ASD through
psychophysical assessment of perceptual outcomes.

Beyond characterization of potentially enhanced multisensory
integration of social robotic cues in children with ASD, it would be
of particular interest to extend these benefits, thereby advancing
therapeutic effects. This could be accomplished through sensory
training and feedback paradigms that have proven useful in other
multisensory integration assessments. The temporal binding win-
dow is somewhat flexible, and multisensory integrative capacity is
capable of adjusting to the temporal properties of environmental
cues. This has been shown in animal studies (Sarko et al., 2012;
Stein et al., 2014; Xu et al., 2012, 2015) and through human psy-
chophysical recalibration effects (Fujisaki et al., 2004; Keetels and
Vroomen, 2007, (Keetels and Vroomen, 2008); Navarra et al.,
2005; Stetson et al., 2006; Vroomen et al., 2004) as well as
through perceptual plasticity and training (Lee and Noppeney,
2011; Powers et al., 2009; Stevenson et al., 2013). A previous
study using simple flash-beep stimuli showed that feedback is
capable of significantly narrowing the temporal binding window of
typically developed adults with as little as 1 day of training (Powers
et al., 2009). Audiovisual training paradigms also resulted in
changes in neural activity in auditory and visual cortices as well as
the posterior superior temporal sulcus (pSTS) (Powers et al., 2012),
a brain area known to be critical in audiovisual integration. Such

Fig. 3. Example trial paradigm for simultaneity judgement task using a representative social interaction of a robot or human shaking the head while saying “no.” Visual motion
stimuli (head shake and lip movement for human stimuli; head shake and eyes lighting up for NAO robot) are presented with corresponding auditory stimuli (e.g., robot head shake
with robotic voice saying “no”). For positive stimulus onset asynchrony (SOA) conditions, the visual stimuli are presented first with a variable delay in the auditory stimuli
(0e400 ms at 50 ms increments, VA conditions; also see Fig. 2A, right side of graph for visual-preceding SOAs). For negative SOA conditions, the auditory stimuli are presented first
with a variable delay in the visual stimuli (0e400 ms at 50 ms increments, AV conditions; also see Fig. 2A, left side of graph for auditory-preceding SOAs).

F. Sartorato et al. / Journal of Psychiatric Research 90 (2017) 1e118

multisensory training paradigms could be applied toward robotic
and human social interactions by incorporating similar feedback
and training to hone the temporal binding window for individuals
with ASD. Through training and feedback informing participants of
response accuracy, temporal binding windows could be narrowed,
allowing individuals with ASD to more optimally filter the some-
times confusing and overwhelming sensory world around them.
Through enhancement of the perceptual benefits of social robots,
these benefits might ultimately be extended to improving human
social interactions and communication.

7. Access to social robot therapies and methodological

Although early intervention strategies using social robot thera-
pies appear to be effective, this efficacy is somewhat hindered by a
lack of affordable, commercially available robots for in-home use
accessible to families of children with ASD. For practical reasons, it
is also beneficial for the robot to be portable, easy to operate to
ensure its utility for children with ASD and their families, and du-
rable enough to withstand occasional rough play from children. In
an effort to address these issues, more recent robotic models with
low manufacturing costs such as PABI (Penguin for Autism Behav-
ioral Intervention, described above) have been developed with the
goal of increasing affordability and accessibility (Dickstein-Fischer
et al., 2011; Dickstein-Fischer and Fischer, 2014). Robotic models
that are more complex, more expensive, and require more

extensive training to operate – such as the Nao robot, with a current
cost of approximately $10,000 – are more amenable to availability
in designated clinical sites and research or treatment facilities.
Although in such cases geographical proximity may be an issue
hindering accessibility to social robot therapy, such availability al-
lows social robots to reach a greater number of children with ASD,
and in a more controlled and observable assessment environment.

Studies involving social robots tend to vary along a number of
different dimensions, including the number of interactive sessions,
sample size, free-form vs. structured interactions, and qualitative
vs. quantitative analysis. Longitudinal studies are needed to follow
children’s progress as they advance through the increasingly
complex social milieu to adulthood. Standardized measures of
targeted behavioral outcomes (e.g., eye contact, turn-taking,
imitation, joint attention, triadic interactions, emotion recogni-
tion/expression, self-initiated interactions, attentional engage-
ment) are also needed to allow cross-study comparisons of robot
design and social interaction efficacy. In addition, increased focus
on generalizability of robotic therapies to human social interactions
should be emphasized in order to maximally improve daily life.


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  • Improving therapeutic outcomes in autism spectrum disorders: Enhancing social communication and sensory processing through …
  • 1. Introduction
    2. Sensory perception & integration: deficits & therapeutic targets in ASD
    2.1. Integration of multiple sensory modalities: challenges and opportunities in ASD
    2.2. Assessing perceptual integration of multisensory cues: the temporal binding window
    3. The neurobiology of social interaction & communication deficits in ASD
    3.1. The neurobiology of perception of social robots
    4. A spectrum of socially-assistive robots
    4.1. Humanoid robots
    4.2. Cartoonish robots
    4.3. An unsuccessful robot model: avoiding the “uncanny valley” of social robotic design
    4.4. Social robots in animal form
    4.5. Social robots in robotic form
    5. Towards an optimal robotic model for use in ASD therapies
    6. Applying systems neuroscience tools to strengthen and extend social robot therapeutic value
    7. Access to social robot therapies and methodological considerations

Int.J. Environ. Res. Public Health 2014, 11, 7767-7802; doi:10.3390/ijerph110807767

International Journal of

Environmental Research


Public Health
ISSN 1660-4601



Technologies as Support Tools for Persons with Autistic Spectrum

Disorder: A Systematic Review

Nuria Aresti-Bartolome * and Begonya Garcia-Zapirain

DeustoTech-LIFE Unit, DeustoTech Institute of Technology, University of Deusto,

Avda. Universidades 24, Bilbao 48007, Spain; E-Mail: mbgarciazapi@deusto.es

* Author to whom correspondence should be addressed; E-Mail: nuria.aresti@deusto.es;

Tel.: +43-943-32-6600 (ext. 2051).

Received: 24 June 2014; in revised form: 18 July 2014 / Accepted: 18 July 2014 /

Published: 4 August 2014

Abstract: This study analyzes the technologies most widely used to work on areas affected

by the Autistic Spectrum Disorder (ASD). Technologies can focus on the strengths and

weaknesses of this disorder as they make it possible to create controlled


reducing the anxiety produced by real social situations. Extensive research has proven the

efficiency of technologies as support tools for therapy and their acceptation by


sufferers and the people who are with them on a daily basis. This article is organized by the

types of systems developed: virtual reality applications, telehealth systems, social robots

and dedicated applications, all of which are classified by the areas they center on:

communication, social learning and imitation


and other ASD-associated conditions.

40.5% of the research conducted is found to be focused on communication as opposed

to 37.8% focused on learning and social imitation skills and 21.6% which underlines

problems associated with this disorder. Although most of the studies reveal how useful

these tools are in therapy, they are generic tools for ASD sufferers in general, which means

there is a lack of personalised tools to meet each person’s needs.

Keywords: ASD; tools for therapy; robots; telehealth systems; dedicated applications;

virtual reality applications


Int. J. Environ. Res. Public Health 2014, 11 7768

1. Introduction

According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), Autistic Spectrum

Disorder is a group of alterations which appear between 12 and 14 months of age and is characterized

by social interaction and communication problems and repetitive behavior [1,2].

Studies show that there has been an increase in ASD in recent years. Several authors have attributed

this to a greater awareness [3], recognition, and diagnosis of the disorder and the fact that less severe

cases being included in the spectrum [4]; in addition to continuous changes in the definition of ASD [5].

However, there is no consensus on the prevalence of ASD because there are many autism-related

syndromes [6], due to diagnoses based on clinical criteria [3].

Nevertheless, studies show that more cases of autism have been detected. There are publications

that show that 1 child out of every 150 or 110 out of every 10,000 children are affected in 2009 [7],

or studies conducted on pre-school age children in Spain that show a prevalence of 8.1% and 11.7% [7].

This indicates that the prevalence of this disorder has risen 78% since 2002 [8,9]. The study presented

by Mayada et al. [10] confirms an average estimated prevalence of 62 per 10,000. Recent results were

presented in March 2014 by Centers for Disease Control and Prevention (CDC) which show that about 1

in 68 was identified with ASD in USA [11].

Due to the increase in diagnosed cases of ASD, software and hardware dedicated to persons with

autism have been developed for several decades. These solutions reinforce ASD sufferers’ strong points

and work on their weaknesses, helping them to increase their vocabulary and communication [12]

skills [13,14]. These studies mostly concentrate on one of the core areas affected by ASD,

communication (the worse their communication problems, the more severe the symptoms of

ASD are [15]).

Tortosa [16] states that Information and Communication Technologies (ICTs) can compensate and

support education of students with special needs, and particularly people with ASD. ICTs make it

possible to create controllable predictable environments; they offer multisensory stimulation, which is

normally visual; they foster or make it possible to work autonomously and develop the capacity for

self-control and are highly motivating and reinforcing [17], encouraging attention and lessening the

frustration that may arise from making mistakes [18].

However, there are authors who maintain that ―computers make persons with autism more autistic‖.

In other words, they believe the use of technology can further isolate ASD sufferers who have

problems in social relationships or can cause them to have obsessive compulsive behavior [19].

However, when used correctly, ICTs may work to improve social interaction due to their multiple

uses and options [16,20].

With new technologies, one are able to get a closer look at the lonely world of autism, prompting a

better understanding of ASD sufferers’ mental state and helping them to develop skills which would

not be possible without the subject-technology interaction. ICTs work to penetrate the isolation of

people with autism and bring them out of the ―world apart‖ in which they live [21,22].

The use of these technologies has been so successful that research using ICTs has increased from

one publication in 1970 to more than 38 a year at the present time [20], appearing not only in impact

journals targeting the social field [23], but also in the technical field [24]. In addition to scientific research,

there are a large number of blogs where family members post how their children interact with them.

Int. J. Environ. Res. Public Health 2014, 11 7769

Specialist literature contains numerous reviews of studies including technology as support and help

tools, proving the benefits of their use. Examples include the work by Ploog et al. [20], Wang et al. [25]

or Scassellati et al. [26]. However, they offer little information on the most recent studies and

concentrate mainly on the areas they target without any division by type of technology or applications.

The following division was used:

 Virtual reality applications

 Dedicated applications

 Telehealth systems

 Robots

The research was further divided according to the area affected by ASD which is targeted in

each study. Several article databases such as Scopus, IEEE Xplore, ACM Digital Library or Web of

Knowledge were consulted to carry out the review, but as most of the articles indexed in these data

bases are also contained in Web of Knowledge, this database has been chosen to make the review.

The following inclusion criteria chosen for this study:

 Articles published between 2004–2014.

 Articles indexed in Web of Knowledge.

 Studies which work on affected area of ASD.

 Studies which incorporate technologies such as virtual reality, robots, telehealth systems or

dedicated applications to detect, diagnose or improve the ASD.

This review is therefore organised in the following sections: firstly, the mixed reality applications

for persons with ASD are analysed. Secondly, the dedicated applications, and thirdly the leading

telehealth systems. Fourthly, the studies conducted with robots. The final section offers a discussion on

the analysed studies and our conclusions.

2. Mixed Reality


The term ―mixed reality‖ has been used for years to refer to virtual reality and augmented reality

technologies. Mixed reality makes it possible to create and develop worlds in which real and

computer-created elements are merged [27,28].

Due to the advantages of using this technology to create controlled and real environments, there is

research that proves how it can be used in a controlled manner as a useful efficient support tool in

areas such as, for instance, health [29–31], defense [32]. In ASD, mixed reality can help us to

understand how children with autism are challenged by a sensory overload and aversion to a variety of

visual and tactile stimuli [33].

The Web of Knowledge was searched with the keywords ―autism‖ and ―virtual reality‖ to find the

leading studies on this technology. As can be seen in Figure 1, the first studies go back to 1996,

with others being carried out from time to time until 2004 when research on the subject increased.

Int. J. Environ. Res. Public Health 2014, 11 7770

Figure 1. Graph of studies on virtual reality and autism.

The virtual reality applications developed for ASD can be classified by the areas they focus on.

The following categories were established for this review: Communication and interaction, social


and imitation skills and other associated conditions.

2.1. Communication and Interaction

Ke et al. [34], developed virtual environments engaging the participants with autism in social

situations and different exercises. The first task was to recognise the body language and facial

expressions of avatars, the second was to communicate with them in a school cafeteria and finally,

interact with them at a birthday party. The researchers carried out an analysis based on observing the

participants and completing questionnaires. They obtained positive results as the children demonstrated

that communication and interaction during the intervention had increased as did their communicative

competences following the tasks.

Brigadoon [35] gives us another example of virtual worlds with a program based on interaction of

people with mental disorders. The aim is to stimulate people with Asperger syndrome or autism to

learn to socialise, providing them with an environment where they can interact with each other.

As Brigadoon was a pilot online community for people dealing with Asperger’s Syndrome and Autism

developed by BrainTalk Communities, there are no published scientific results.

In 2006, Parson et al. [36] studied the behaviour of two adolescents with ASD in two virtual

environments, a café and a bus. In this study, the authors proved that the adolescents significantly

interpreted the scenes and appreciated the opportunities to maintain a dialogue and respond


although they continued to show repetitive behaviour and interpret the situations literally. Mitchell et al.

followed this same line of research [37] created a virtual coffee shop. 6 adolescents with ASD were

shown 3 sets of videos of real situations taking place in coffee shops and cafés followed by the virtual

environment. They had to say where they had decided to sit and why. This answer was analyzed and

coded by 10 evaluators. Half of the participants were shown the virtual environment between the first

Int. J. Environ. Res. Public Health 2014, 11 7771

and second set of videos and it was shown to the second half during the second and third set of videos.

The researchers found that there were cases of significant improvement, directly related to the time

spent in the virtual world when deciding and explaining where they chose to sit. Stickland

et al. [38]

developed a tool called JobTIPS which made it possible to teach job interview skills to people with

high-functioning autism. They used visual support aids, videos, guides on the theory of mind and

virtual worlds where they practised these skills. Twenty two young people took part in the experiment

to check the effectiveness of the program. Half of the young people completed sessions

with the

programme while the other half that formed the control did not use the system. Following the

experiment, the participants who had used the programmer showed significantly better verbal


during the interview than the control group.

2.2. Social Learning and Imitation Skills

Researchers Josman et al. [39] developed a safe environment using virtual reality technology

which enabled persons with ASD to learn how to cross the street. Six children with ASD formed the

experimental group and six children with neurotypical development formed the

control group.

The researchers concluded that persons with ASD learned the skills needed to make the right


when crossing the street in a virtual environment and thus, the knowledge acquired could be applied to

real situations.

Virtual environments have also been studied to help learn skills such as playing. Herrera

et al. [40]

conducted two case studies on children with autism in which they evaluated this skill with virtual

environments. The findings showed improvement in play skills following the intervention.

Fabri et al. [41] centered their research on how persons with autism interact with avatars capable of

facial expressions showing emotions (happiness, sadness, anger and fear). In the first stage of the

experiment, the participants (34 young people diagnosed with ASD, average age of 9.96) had to

choose the emotion the avatar was expressing from a list. In the second stage, the avatar appeared in a

social environment and the participants had to interpret what emotion the scene involved. In the third

and final stage, the participants had to select what caused the emotion the avatar was expressing from a

list of events or situations. The authors checked that 30 of the participants understood the emotions of

the avatars and used them appropriately. However, the other four participants, who were in the group

that described themselves as having severe autism, had a real difficulty in understanding the emotional

representation of the avatars.

Ehrlich et al. [42] developed a 3D virtual world called Animated Visual Supports for Social Skills

(AViSSS) at the University of Kansas in 2008. This system enabled people with Asperger syndrome to

work on social skills using different environments and situations shown on the platform. Participants

had to choose how to behave or select objects. This platform afforded them the opportunity to practice

different social situations without the tension or anxiety involved in the real world. During the initial

tests, the authors concluded that the students with ASD did not respond well to the virtual avatar,

virtual teacher specifically, due to the fact that, they appeared to perceive teachers as being uninterested,

impatient to deal with them.

Int. J. Environ. Res. Public Health 2014, 11 7772

2.3. Other Conditions

This technology has also been used to motivate people with autism to do exercise. Finkelstein

et al. [43]

developed a game called Astrojumper. Users had to dodge virtual objects that appeared on the screen.

Herrera et al. [44] carried out a pilot study which took advantage of the game provided by Kinect.

They developed a set of educational games in which children did exercise (using their bodies as the

control mechanism) and which also made them more aware of their own bodies. Studies have also

been conducted to examine how people with ASD interact with the real world. Fornasari

et al. [45]

created an urban environment where they compared the behavior of neurotypical children and


with ASD. It consisted of two exercises. In the first one, the children explored the environment freely

and in the second, they went round the environment to fulfil the goals set. The researchers found that

there were no differences in behaviour between the two groups in the second task. However, in the first

task, children with ASD took less time to explore the environment than the neurotypical children did,

with significant behavioral differences between the two groups.

2.4. Conclusions

Good results have been obtained by using virtual reality applications as therapeutic tools, thus

helping people with autism to recognize emotions and improve their social and cognitive skills [46].

Virtual reality makes it possible to create safe environments where they can learn rules and repeat

the tasks. Furthermore, interacting with avatars where social situations are replicated enables patients

to work on these situations and find more flexible solutions. This means that virtual environments may

be good instruments to work on social skills with ASD sufferers [47,48].

This technology makes it possible to create avatars or more real looking characters to enable

participants with autism to work on facial expressions and emotions and recognise them [41,49] while

also creating controlled environments to make them feel safe [50,51]. Therefore, this


provides advantages that can be used as a support tool in therapy. Verbal and gesture-based


can be worked on in virtual reality or mixed reality environments, achieving effective

neurorehabilitation in children [24,25]. Table 1 contains a summary of the studies analyzed.

3. Dedicated Applications

In this paper, technological tools targeting people with autism are called dedicated applications

(virtual reality is not used). They are designed to be used on computers, tablets or mobile telephones.

Applications dedicated to people with autism are mostly support tools to facilitate or assess their skills

when communicating, with a focus on social skills. This study therefore analyzes the tools found to be

most significant. They are divided into the following groups: (1) Communication (2) Social learning and

imitation skills (3) Other associated conditions.

The keywords used to search the most relevant studies on this technology in the Web of Knowledge

were ―autism‖ and ―computer application‖. As shown on the graph (see Figure 2), the first studies go

back to 1995, with research having been conducted off and on since then. Research on the subject

began to increase after 2007. 2010 was the year most research on this topic was carried out.

Int. J. Environ. Res. Public Health 2014, 11 7773

Table 1. Studies on mixed reality systems.

Author Year Country



Age Diagnosis Area Method Results Classification


et al. [34]

2013 USA 4 Children – 4–5 High-







+ Persons

Communication and

interaction during



Social learning

and skills




2006 – – – – Social


Virtual-Reality Brigadoon. Pilot

online community


and interaction


et al. [36]

2006 UK 2 adolescents – – ASD Social




café and bus


interpreted the scenes

and responded

and interaction


et al. [37]

2007 UK 6 teenagers – – ASD Communication Virtual



Improvement related

to time spent when

they were making

and interaction


et al. [38]

2013 USA 22 teenagers – 16–19 High-functioning


Job interview



videos, Theory

of Mind guides

Improvement on

verbal skills

and interaction


et al. [39]

2008 Israel 6 children 6 children – ASD Social skill:

Cross the street



Learning the skills

needed to make right

Social learning

and imitation



et al. [40]

2008 Spain 2 children – 8:6 ASD Play skills Virtual


Play skills


Social learning
and imitation


et al. [41]

2007 UK 34 young


– 7–16 18 Asperger and

16 with severe


Social skills Virtual avatars 88.3% of


understood the

emotions of avatars

Social learning
and imitation

Int. J. Environ. Res. Public Health 2014, 11 7774

Table 1. Cont.

Author Year Country
Age Diagnosis Area Method Results Classification


et al. [42]

2009 USA Adolescents – – ASD Social skills AVISS virtual




Participants did not

respond well to the

virtual avatar

Social learning
and imitation


et al. [43]

2010 USA – 8 people 4 (11–16 years)

2 (18–25 years)

2 (40–50 years)

Neuro-typical Physical



a virtual

reality game,

dodge objects

Pilot study

which works on

physical exercise




et al. [44]

2012 Spain – – – – Motor skills Kinect and



Pilot study which

made children

more aware of

their own bodies




et al. [45]

2013 Italy 16 children 16 children – ASD Behavior Urban virtual






1° task: children with

ASD took less time

to explore

environment than

control group.

2° task: no behaviour



Int. J. Environ. Res. Public Health 2014, 11 7775

Figure 2. Graph of computer application and autism research.

3.1. Communication

People with autism experience serious difficulties in social interaction and conversation [52], so the

majority of the applications center on improving their communication. In 2008, Grynszpan et al. [53],

developed software especially for people with ASD. It consisted of three games. These games were used to

work on one of the areas affected by this disorder: communication. The software included subtitled

dialogues that expressed irony, sarcasm and metaphors, in addition to faces showing emotions. Participants

had to understand the situation shown and respond correctly. Ten adolescents diagnosed with ASD and 10

neurotypical adolescents took part in the study. The software was used once a week for 13 weeks.

The researchers used the results obtained in the first and last sessions to evaluate each participant’s skills.

The results showed that the adolescents with ASD performed poorly on rich multimedia interfaces because

they lacked initiative when organising the information given in the multimodal sources.

In 2011, the Orange Foundation and the Dr. Carlos Elósegui Foundation at the Guipuzkoa

Polyclinic developed software that facilitated communication for people with ASD. It is called

e-Mintza and uses tactile technology and icons, symbols such as pictograms and ARASAAC graphics.

E-Mintza easily adapts to users’ needs. It also fosters their automony via a personalized agenda [54].

In line with this type of systems, a communicator called Piktoplus was developed. Piktoplus is a tool

based on the System of Augmentative and/or Alternative Communication (SAAC). It was designed to

facilitate communication for anyone who cannot use and/or understand verbal speech. It consists

of a tactile table formed by pictograms that enable the user work on: language, behavior guidelines,

motricity graphs and specific cognitive areas by playing [55]. ZacPicto is another similar system.

ZacPicto is tool created to help parents and professionals to work with people with autism. The

programme provides a visual organizer which makes it possible to organise and structure all the

activities as well as a communication space via a social network for everyone involved in caring for

people with autism: parents, teachers, therapists [56,57].

Applications for tablets or PDAs are another line of research. Torii et al. [58,59] developed Lets

Talk! in Japan. Lets Talk! is a programme for personal digital assistant (PDA) systems that help users

Int. J. Environ. Res. Public Health 2014, 11 7776

to communicate by selecting images and sounds from the programme. The system’s effectiveness and

usability was checked with a 9 year-old child with autism. After using the system, his bad behaviour

improved as he learned to express his thoughts and interests appropriately with the application.

In 2003, Ganz et al. [60] proved the efficiency of the use of tablets as communicator systems over

conventional communicators. Three people with ASD took part in the study, two of whom quickly

learned to use the system and stated that they preferred it over conventional systems. However, the third

participant was not skilled at using it and preferred the former systems.

3.2. Social Learning and Imitation Skills

Research on the effectiveness of music in therapy with people suffering from ASD has been

conducted since 1964 [61,62]. These studies show how therapies including music help to persons with

autism to learn new concepts and skills [63–65]. Music has therefore been included in applications used

as treatment tools. For instance, in 2009, Hoelzl et al. [66], developed a prototype tool to create music

called ―Constraint Muse‖ for high-functioning children with autism or Asperger’s syndrome and people

suffering from Parkinson’s Disease. The system used Nintendo’s Wii control to make it easy to use [67]

and create music. It also fostered collaborative play by allowing several people to create music together.

Studies were also carried out to check emotion recognition skills. Tanaka et al. [68] used the emotion

skills battery Let’s face it! in 2012, in which they compared 68 ASD children and 66 neurotypical

children as they labelled social emotions such as happiness, anger, disgust, surprise, etc. shown on


The ASD children had worse results than the control group when naming happiness, sadness, disgust and

anger. They also analysed how the children examined the facts and found that the children

with ASD

paid more attention to the mouth than the eyes while it was the opposite in the neurotypical children.

Another study was the one conducted by Hulusic et al. [69], in which a framework was created to help

people with autism to learn new skills. They developed four games that taught the participants pointing

skills during the games. This skill is thought to be necessary to learn other new ones. The study proved the

usability of the tool and obtained very positive results since the children participating were able to use it

easily. The children also transferred the knowledge they acquired to other environments.

Chanchalor et al. [70] demonstrated that computer games which included art activities and songs

improved the capacity of the five participating children to learn colors and develop their imagination after

using them for 6 weeks. Thus, they confirmed that adapted tools are useful to work on and improve skills.

3.3. Other Associated Conditions

Skills related to play and the imaginations are also studied by using dedicated applications for

persons with autism. One such example is by developing systems that include story tellers [71].

Murdock et al. [72] used an iPad that told stories develop communication while playing. Four small

children with autism took part in the study. They played with videos featuring dolls that produced

interactive dialogues and encouraged the children to participate. After using the system, the three

participants managed to increase dialogue and even produce new dialogues during the game.

However, one of the children showed no improvement after using the game. Another interesting study

was carried out by Dillon et al. [73] in 2011 in which an application enabled children with autism to

invent stories. Through the children’s creations, they were able to analyze writing skills and

Int. J. Environ. Res. Public Health 2014, 11 7777

imagination in children with autism in comparison to neurotypical children. They found that children

with autism and neurotypical children invented real stories and fantasies and that both groups invented

more stories based on real facts than fantasy. However, in both groups the logic used was better in the

fantasy stories. The researchers found that the two groups used different aspects of the application to

create their stories. The neurotypical children made no errors whereas the children with autism did.

This proved that this disorder affects the imagination.

Sarachan et al. [74] worked on the imagination via the Scratch programme. Children used it to

invent their own stories and games and it enabled children with autism to develop and strengthen

problem-solving capacity and creativity (areas that this disorder usually affects).

An application called ZacBrowser is also of interest. ZacBrowser is a browser developed especially

for children with autism and autism spectrum disorders. It is divided into several categories (aquarium,

television, games, music, stories and blackboard) and leads the child to webpages with content for

children, thus avoiding the possibility of entering unsuitable pages or those that contain too many

stimuli that could distort the user’s attention [75].

Computer games have also been developed which explore prosodic focus and linguistic


of spoken phrases [76]. The children listened to pairs of pre-recorded phrases whose content and

intonation varied in the practice phase and then heard a recombination of them in the actual test phase.

The children had to select one of the two phrases whose content and prosody varied. The researchers

found that during the practice phase, the children with autism made similar selections when choosing

phrases according to content or their prosodic features while the children with normal development

showed a clear preference for content over prosody. However, both groups discriminated between the

practice stimuli and the recombination of test stimuli.

Their capacity for expression was studied through the system designed to evaluate syntactical

awareness [77]. The children learned to touch words on a screen in the correct sequence to see the

corresponding animation. Although the results varied, it was found that the users lacked syntactical

awareness but their command of basic syntax in the non-voice domain was higher than what they

howed when speaking.

3.4. Conclusions

Applications of this type have been used to work on the areas affected by autism and conditions

related to the autism spectrum disorder, mainly concentrating on creating applications that help

persons with autism to communicate through images and sounds.

These systems are widely accepted because they are simple to use and contain very intuitive tools,

since they work with everyday items. However, it is important to remark that these are pilot studies so

it must still be demonstrated that users can transfer these new skills to their everyday lives.

It is therefore important to continue developing these systems and further research in the field to tackle

key challenges such as communication and interaction. Including the human component in systems is

considered essential. In other words, another person must take part in the system, thus obliging autism

sufferers to communicate. Table 2 shows a summary of the most relevant studies on dedicated

applications which are analyzed in this paper.

Int. J. Environ. Res. Public Health 2014, 11 7778

Table 2. Studies on dedicated applications.

Author Year Country
Age Diagnosis Area Method Results Classification


et al. [53]





– ASD Communication


Subtitled dialogues

(irony, sarcasm and

metaphoras); images

of facial expression

Participants with ASD

performed poorly on

rich multimedia inter-

and interaction


orange [54]

2011 Spain – – – ASD Communication Emintza:

Communicator using


Pilot study. Software



and interaction



2012 Spain – – – ASD Communication Piktoplus:

Communicator using

System which works on

language, behaviour

guide-lines, motricity.

and interaction



2012 Spain – – – ASD Communication ZacPicto:

Communicator using

Tool which

helped parents.

and interaction


et al. [58,59]

2013 USA 1 – 8 years Autism Communication Lets Talk! Bad behaviour and

learning to express



and interaction


et al. [60]

2013 USA 3 children – 3–5


ASD Communication Tablet as a


2 of 3 children

preferred the

new system

and interaction


et al. [61]

2013 – 5 children

with ASD

– 11–15


ASD Abilities to

learn about


Art activities, game

and folklore on



Improve on learning

colors and




Social learning
and imitation


et al. [66]

2009 Germany – – – Asperger or



play and


Constraint Muse:

Music +



Prototype tool to create

music with Nintendo

Wii control
Social learning
and imitation

Int. J. Environ. Res. Public Health 2014, 11 7779

Table 2. Cont.

Author Year Country
Age Diagnosis Area Method Results Classification


et al. [68]

2012 Canada 68 66 typically


– ASD Social deficits



Let’s Face It!

Emotion Skills


Children with ASD paid

more attention to the

mouth than eyes

Social learning
and imitation


et al. [69]

2012 USA 4 children

with ASD

– – ASD Teaching basic

skills and


Four games for

developing matching,

pointing out and

labeling skills

The children transferred

the knowledge they

acquired to other


Social learning
and imitation

Chanchalor et

al. [70]

2013 5 children

with ASD



ASD Social deficits Activities in the

computer media

Improvement on

abilities to learn about


Social learning
and imitation


et al. [72]

2013 USA 4 children – 49–52


ASD Communication iPad play story 3 of 4 participants

increased dialogue

and produced

new dialogues





et al. [73]

2011 UK 10 children 10 children Average

8.96 and


ASD (High-


Imagination Application based on

creating stories

Both groups invented

more stories based on

real facts than fantasy,

but the clinical group

made mistakes



et al. [74]

2012 USA – – – ASD Creativity Scratch: Create

stories and games

Developing and



capacity and creativity


Int. J. Environ. Res. Public Health 2014, 11 7780

Table 2. Cont.
Author Year Country
Age Diagnosis Area Method Results Classification


et al. [76]

2009 USA 9 children 9 children – ASD (Low-


Prosodic focus

and linguistic


Computer game Children with ASD

made similar selections

according to content or

prosodic features.

Control group showed

preference for content

over prosody




et al. [77]

2013 Scotland,


9 – – Low-


Language Learning computer

game: 3task (2 words

Noun Verb, 3 words-

Noun Verb Noun and

4 words-Noun Verb

Preposition Noun)

Users lacked

syntactical awareness



et al. [78]

2006 UK 19 adults 24 adults Asperger

and High-



emotions in

faces and voices



Users learned to

recognize a variety of

complex emotions and

mental states.

Social learning
and skills



Int. J. Environ. Res. Public Health 2014, 11 7781

4. Telehealth Systems

There are dedicated applications to help not only persons with ASD but also their families. This is

the case of telehealth systems. They enable patient-doctor information exchange without having to

go to the medical facilities, reducing the costs involved [79,80]. For this reason, research on the

benefits of telehealth systems cover many fields of health [81–83] and are directed to adults

as well as children [84–86].

Due to the benefits these systems offer, the concept was transferred to the world of ASD and centers

on helping family members caring for people with autism. The keywords for the search on the Web of

Knowledge were ―autism‖ and ―telehealth‖. As shown in the graph (see Figure 3), this technology was

not included in the field of ASD until 2004 although it has increased, with five impact studies

published in 2013.

Figure 3. Graph of studies on telehealth and autism.

These studies mainly focus on helping family members of people with ASD to gain new knowledge

about the disorder and as a tool to obtain information when diagnosing or determining treatment.

Therefore, the following division was made: (1) telehealth systems for use by family members and

(2) telehealth for diagnosis and treatment of ASD.

4.1. Telehealth for Use by Family Members

One example is that developed by Baharav et al. [87], which aimed to inform family members how

to continue their children’s treatment in the home. They therefore compared the telehealth system

developed to the traditional clinical model (speech and language therapy sessions) by using it once a

week. The parents of two children diagnosed with autism took part in the study and stated that the

telehealth system was as useful as traditional therapies, enabling them to continue with their children’s

treatment from the home.

Int. J. Environ. Res. Public Health 2014, 11 7782

In 2012, researchers Wacker et al. obtained a similar result in their study. Wacker et al. developed a

system which enabled family members to receive information on functional communication to identify

and reduce behaviour problems [88,89]. The same year, Vismara et al. conducted a study which

showed that telehealth systems made it possible for family members of persons with ASD to learn

early intervention techniques and put them into practice in the day to day [90]. Following this line of

research, Kobak et al. [91] assessed a web-based system that gave parents access to information about

how to improve interaction with their family members with ASD. It was based on evidence-based

practice and use of this system to maximize learning. They evaluated the effectiveness of tutorials and

the family members’ knowledge before and after the experiment. They found that family members’

knowledge about how to communicate with their children improved and they also felt capable of using

these communication techniques on a daily basis.

4.2. Telehealth for Diagnosis or Treatment of ASD

Researchers Oberleitner et al. conducted numerous studies on telehealth systems for use in

diagnosis and treatment [92–94]. They developed a ―tele-behavior‖ health system that enabled family

members and carers to compile accounts of the spontaneous behavior of persons with ASD which was

later analyzed by specialists [92]. This behavior was captured by using video technology, which made

diagnosis quicker and more accurate [93,94].

Another telehealth system which has given optimal results in this field is the one developed by

Parmanto et al. [95] in 2013. The system included videoconferences, recordings, images and videos,

etc. facilitating face to face assessment of persons with ASD without having to go to therapies

or clinics, etc.

Resee et al. [96] found a gap between the first suspicion of autism and diagnosis, above all in rural

environments. They therefore developed a system by which persons suspected to have autism were

assessed via telehealth. The researchers assessed the items which appear in the Autism Diagnostic

Observation Schedule (ADOS)—Module and the Autism Diagnostic Interview-Revised (ADI-R).

Their findings showed that the reliability of the system was similar to face to face sessions.

Gorini et al. [97] added the efficiency of virtual environment systems to telehealth systems to

improve the latter. They developed the virtual world Second life, used as a stage where different

disorders such as ASD can be treated. Through virtual reality, avatars were included, which allowed

users to interact and improve patient-professional interaction and communication.

4.3. Conclusions

This subsection analyzes the most relevant telehealth systems (see Table 3). Early intervention in

autism can improve the quality of life of people diagnosed with this disorder. However, not all of them

receive early intervention [91]. Parents are the first to detect any problem with their children and their

effectiveness as intervention agents has been proven. However, supervision by highly qualified

professionals is needed [87]. For this reason, the efforts made in the field of telemedicine for people

with ASD focus on creating tools that help family members or clinicians to gain knowledge about

ASD [98].

Int. J. Environ. Res. Public Health 2014, 11 7783

Table 3. Studies on Telehealth systems.

Author Year Country
Age Diagnosis Area Method Results Classification


et al. [87]

2010 USA Parents of

2 children

with ASD

– – ASD Compare a


model of twice-weekly

speech and language

therapy sessions and



Traditional model




Telehealth system

as useful as



Telehealth for use

by family



et al. [88,89]

2013 USA 20 young




ASD Problem


(conducted functional)




information on


communication to

identify and reduce

behavior problems

Telehealth for use
by family


et al. [90]

2012 USA 9 families with


ASD Language and

imitation skills

Helping parents

understand and use

early intervention


Systems facilitated

learn early



Telehealth for

family members



2011 USA 23 parents with

a child between

18 months

and 6 years

with ASD

– – ASD Parents’ knowledge System usability

scale(SUS) and user


questionnaire (USQ)


with their children

Telehealth for use
by family



2004 USA – – – ASD Facilitate the capturing

and communication of

spontaneous patient


Video Technology Communication


Telehealth for the

diagnosis or

treatment of ASD



2006 USA – – – ASD Diagnosis and

treatment of autism

Video Technology

Diagnosis quicker

and more accurate

Telehealth for the
diagnosis or

treatment of ASD





Int. J. Environ. Res. Public Health 2014, 11 7784

Table 3. Cont.

Author Year Country
Age Diagnosis Area Method Results Classification


2007 USA – – – ASD Child’s behaviors Video-capture

Diagnosis quicker
and more accurate
Telehealth for the
diagnosis or
treatment of ASD



2013 USA – – – ASD Diagnosis or treatment

of adults with ASD


stimuli presentation,

recording, image

and video


and electronic

assessment scoring

Facilitating face to

face assessment

Telehealth for
diagnosis or
treatment of ASD


et al. [96]

2013 USA 10 children 11 3–5


ASD Clinicians’ ability to

assess autism via


Videoconferencing The reliability of

the system was

similar to face to

face session

Telehealth for
diagnosis or
treatment of ASD

Gorini et al.


2008 Italy 48 participants ASD Language skills Telehealth system

with virtual reality

Improvement on



interaction and

Telehealth for
diagnosis or
treatment of ASD




Int. J. Environ. Res. Public Health 2014, 11 7785

These systems have been widely accepted and received positive evaluations from families and

doctors because the service is easy to use and convenient to access at any time. The systems make it

possible to reduce health care costs [94,99]. However, there are gaps in these systems because they do

not include tools to work on the areas affected by ASD through games and they target family members

rather than people with ASD [87,100]. Nevertheless, telehealth systems are useful tools that allow for

fluent communication between clinicians and family members, providing the latter and people with

ASD a great deal of support.

5. Robots

In addition to the systems and technologies described, there are studies that analyze the behavior of

people with ASD in response to robots designed to work on areas affected by this disorder.

Robots have interesting characteristics that make them useful as tools to treat ASD [22,101,102].

Robots show predictable behavior, produce controlled social situations and interact with persons in a

simple manner. This makes people with ASD feel less anxious by making social situations less

complex [103,104]. The keywords used for the search on the Web of Knowledge were ―autism‖ and

―robots‖. The first studies date from 1999 and have gradually increased with many being carried out

in 2010 (see Figure 4).

Figure 4. Graph of studies on robots and autism.

Like the other technologies explained above, research on the use of robots in therapy specifically

focuses on social communication and social learning and imitation skills, with promising results.

5.1. Communication and Interaction

The literature on social communication contains different studies that aim to analyze the behavior

of autistic children when interacting with robots equipped with social capacities, which are used

in therapies. Huskens et al. [103] researched and compared the effectiveness of robots in social

Int. J. Environ. Res. Public Health 2014, 11 7786

interventions based on applied behavior analysis. These researchers proved that intervention with

robots was just as efficient as human intervention when motivating children with ASD to ask questions.

Goodrich et al. [104] included a robot managed by the Wii control in 16 treatment sessions.

The children interacted with it for 10 min. The robot consisted of a screen which showed the robot

face making different expressions. The researchers analyzed the children’s behavior with the robot,

including language, gestures, eye contact, imitation and demonstrations of affection. They found that

the children were very motivated to interact with the robot and after the treatment with the robots,

they interacted more with the clinicians than at the beginning of the study.

Kim et al. [22] conducted an experiment in which they found that children with ASD communicated

more with adults when they played with a dinosaur robot. They ran three experiments with 23 children

with ASD. The children had to interact more with one adult than another, or with a tactile screen or a

dinosaur robot. Besides communicating more with the adult, they showed that children talked as much

to the dinosaur robot as to the adult in charge of the session.

Although robots help people with autism to communicate, Lee et al. [105] conducted two studies

which analyzed the social behavior of autistic children with robots. Fifteen children with ASD and robots

with a ―face‖ but which could not talk took part in the first study. In the second study, they analyzed the

verbal capacity between the robot and six children with low-functioning autism comparing it to the

children’s interaction with an adult. The findings of these experiments showed that, in the first case,

the robots with faces foster work on social skills and facial expressions in children with autism but have

no influence on the development of other skills. However, in the second case, they proved that the

children interacted better with robots that could talk, following their verbal instructions and facial

expressions better than with persons. Thus, robots that can talk may be an option in therapies.

However, not all the children with ASD reacted the same way to the robots. One example is the

study by Tapus et al. [106]. They used the robot Nao which is capable of imitating the children’s

movements in real time, analyzing the looks, smile, arm movements, etc. The study showed how two

of the four children with ASD showed no change for the parameters analyzed. The other two

participants showed greater eye contact with the robot than the other child and only one of the children

made more spontaneous movements when interacting with the robot than with the other children.

5.2. Social Learning and Imitation Skills

Jordan et al. [107] studied the use of robots to work on attention, communication and social skills in

adolescents with ASD. They recorded parameters while the participants played the card game called

Face Match in different environments: with a humanoid robot, a Smart Board and the cards.

The participants played for three days and the researchers recorded their behavior as they interacted

with the three environments. Following the sessions, the researchers found that although there were

individual behavior patterns during the three game modes, repetitive behavior was reduced when the

adolescents played with the robot or the Smart Board.

Imitation is another skill that can be developed with robots. Srinivasan et al. [108] found that after

eight sessions using robots to work on this skill, the child with ASD improved in imitation specific

tasks when using the robot. Following this line of research, Srinivasan et al. [109] studied how

children with ASD imitated a robot by making karate and dance movements. The researchers ran eight

Int. J. Environ. Res. Public Health 2014, 11 7787

practice sessions with 15 typically developing children and four children with ASD/ADHD and eight

test sessions in which they evaluated the children’s evolution. The results showed that the participants

made fewer errors during the test than during the practice session, thus improving imitation-specific tasks.

However, these robots developed do not provide an individualized system for each ASD sufferer.

For this reason, Bekele et al. [110] developed a robot with augmented vision with a camera network

that obtained the head tracking in real time. The robot is capable of adapting and generating

reinforcement and messages through head movements made by the person with ASD. This fosters

work on social skills with each child individually.

Another capacity robots used in autism therapy have been equipped with, besides facial

expressions, is the ability to tell stories in which they teach children with ASD how to act in social

situations. This is the case of the robot Probo developed by Vanderborght et al. [111] which shows

children how to react to everyday situations by saying: ―Hi‖, ―Thanks‖ or ―Share toys‖.

5.3. Conclusions

Robot toys can help special needs children to work on social skills, learn new skills and discover

the different game modes, in other words, show them that collaborative games also exist [110].

Thus, social robots may become very useful tools in therapy with ASD children [101–113].

Due to the inclusion of social robots in therapy, one has even observed how the children’s limited

interests and repetitive behavior have improved. However, although robots are an effective tool, we must

not forget that collaboration from people is always needed in therapy or treatment [103]. All robots do not

achieve the same objective so it is interesting for therapy robots to be equipped with voice technology in

order to foster social skills in persons with autism [105]. Table 4 lists the most relevant research.

6. Discussion

This article presents a review of the most relevant applications and technologies developed from

2004 to 2013. Most of the results of this research, mixed reality tools, dedicated applications,

telehealth systems or robots have been very positive, usually reaching the objective set for each study.

All the studies show that technologies make it possible to work on the areas affected by the disorder,

creating controlled environments where ASD sufferers feel safe and comfortable [105].

However, which technology is the most suitable for use in therapy? Can we conclude that these

technologies really serve to teach new skills that improve these people’s quality of life?

After having analyzed the studies one by one, it is important to take a closer look at the advantages

and disadvantages of these technologies as a whole and compare them to find a satisfactory answer to

these questions.

This study shows how research has tended to more studies on the effectiveness of applied

dedications and mixed reality for this group in recent years (see Figure 5). As we have mentioned in

the section on telehealth systems, this concept is very recent so there is not as much research as on the

other technologies (see Figure 5). However, after having demonstrated the efficiency of these systems

with other groups, they are gradually being included to help people with ASD and their families or

carers [81,86].

Int. J. Environ. Res. Public Health 2014, 11 7788

Table 4. Studies on the use of robots in therapy for children with ASD.

Author Year Country Sample Control Group Age Diagnosis Area Method Results Classification


et al. [22]

2013 USA 24 children – 4–12 ASD Social Behavior Interaction with

(1) another adult


(2) a touchscreen

computer game,

and (3) a social

dinosaur robot

Children talked as

much to the

dinosaur robot as

to the adult

Social learning
and imitation


et al. [103]

2012 Netherlands 6 children – 8–14 ASD Self-initiated



conducted by a

human or by robot

Intervention with

robots was

just as efficient

as human

and interaction


et al. [104]

2012 USA 2 children – 3 ASD Interaction Social Robots After the

treatment with

the robots,


interacted more

with the


and interaction


et al. [105]

2012 Japan 21 children Children

6–15 years

– ASD-Low-


1-robots with social


skills 2-robots

with verbal



The children

interacted better

with robots that

could talk

and interaction




Int. J. Environ. Res. Public Health 2014, 11 7789

Table 4. Cont.

Author Year Country Sample Control Group Age Diagnosis Area Method Results Classification


et al. [106]

2012 France 4 children – – ASD Social skills Nao robot (eye gaze,

gaze shifting, free

initiations and

prompted initiations

of arm movements,

and smile/laughter)

2 of 4 participants

showed greater

eye contact with

the robot than the

other child

and interaction


et al. [107]

2013 New


3 adolescents 3 adolescents – ASD Attention,


Social skills

Memory card

matching game

(robot, Smart Board,

playing cards)

Reduction of


and interaction


et al. [108,109]



USA 2 children

15 typically





USA 2 children Imitation-specific



15 typically


et al. [110]

2013 USA 6 children 6 typically


– ASD Deficit area of

early social


humanoid robot

with augmented


Robots promoted

social skills work

with each child


and interaction


et al. [111]

2012 USA 4 children – 4–9 Austism Social skills


Robot Probo

(story teller)

Learning how

react to everyday


Social learning
and imitation


Int. J. Environ. Res. Public Health 2014, 11 7790

Figure 5. Summary of the technologies reviewed.

Analysis of the studies on mixed reality, robots and dedicated applications related to the skills they

focus on shows that 40.5% of this research highlights communication and interaction (see Figure 6).

This is because communication and interaction is one of the core areas affected by the disorder and

therefore a key aspect in therapy and the home [1,2]. In the research reviewed, all the technologies

targeting communication have obtained good results and satisfactorily met the objectives set by the

researchers [34–38,53–60,103–106].

Figure 6. Analysis of the areas treated by using mixed reality, dedicated applications

and robots.

Int. J. Environ. Res. Public Health 2014, 11 7791

However, it has been observed that social robots and virtual reality are the most suitable technologies

for work on communication and interaction because they focus on involving the users or participants in

social situations they need to be capable of coping with. However, 37.5% of the studies on dedicated

applications (see Figure 7) mainly center on providing tools that help to communicate by generating

phrases with visual support aids. Put differently, users form phrases by using technological support for

images, audio and texts that are reproduced when they need to communicate with other people to

express their needs or feelings. However, it has not been demonstrated that they improve or learn to

communicate with these applications [60].

Virtual reality makes it possible to create environments and avatars that can more realistically

reflect the social situations people may be involved in and show them how they should behave in these

situations. They produce situations in which the user has to communicate with other virtual components

or interact with them [34–38]. Robots equipped with social and verbal capacities make it possible to

work on robot-person interaction because they attract the users’ attention.

Figure 7. Analysis by technology and area being treated.

As for social learning and imitation skills, 37.8% of the studies analyzed focus on these areas

(see Figure 6). When analysing the research by technologies, 45.5% of the mixed reality studies,

31.3% of the dedicated applications studies and 40% of the research with robots focus on this aim

(see Figure 7).

The researchers conducting these studies have indicated that these technologies can also foster

learning and imitation of several social skills, which is the case of mixed reality, by crossing the street

or learning play skills [39]. The dedicated applications center on working on facial expressions [68],

imagination [66], or learning colors [70] and robots focus on imitation [108,109].

Int. J. Environ. Res. Public Health 2014, 11 7792

However, these studies have their limitations as only the tools developed with robots are

collaborative [112], in other words, the only tools that make it possible for more than one person to

interact simultaneously. Thus, most of the research involves people with autism working individually

with the tool, although there is a therapist present and directing the session.

When analyzing the results of these tools to work on problems associated with the disorder,

we found that 21.6% of the studies center on these problems (see Figure 6). When checking the studies

by each technology, 27.3% of the mixed reality studies and 31.3% of the dedicated applications studies

center on associated conditions (see Figure 7).

Thus, we found studies focused on areas such as imagination [71], creativity or language [76] in the

case of dedicated applications and motor skills and behavior in the case of mixed reality. Mixed reality

is thought to be the best technology for work on motor skills because it includes hardware such as

Kinect and makes the user’s own body carry out all the actions in the system.

Mixed reality systems are thought to be the best technology for work on motor skills because they

include hardware such as Kinect in which the user’s body carries out all the actions in the system.

In other words, it turns the user into the control mechanism without the need for an input device

such as a mouse or keyboard. With the further advantage of games, users perform tasks while

enjoying themselves and forget that they are exercising and thus working on motor skills [114].

However, this area has not been treated with dedicated applications because they are not active tools.

It was not possible to compare telehealth systems and robots, dedicated applications and mixed reality

because they have not concentrated on the areas of communication and imitation, social learning skills or

other associated conditions. This is due to the fact that telehealth systems have only recently been used

to work with autism and research centers on establishing contact between family members and

clinicians to show them aspects of the disorder or obtaining images of people with ASD to analyze

their behavior. Although these systems have proven successful as support tools to treat different

pathologies [115], facilitating person-clinician communication, there are no studies that include

exercises for persons with ASD to do at home. This would make it possible to use objective variables

to check ASD users’ evolution.

A comparison has been carried out on the features of these technologies in the research for which

they were used. As we can observe in Table 5, key parameters were analysed: usability of the systems,

if the systems are invasive, accessibility, how the data are collected, efficiency and cost.

Analysis of the use of these technologies in the research showed that they have high usability and

accessibility because they are specifically designed for people with ASD so they are simple to use.

Invasive techniques have not been used in the studies reviewed. They do not pose risks to users or

intrude in any way [11–112]. Data collection varies depending on the platform, with the mouse input

device as the most widely used. The users also used their own bodies to interact with the mixed reality

system [43], and with robots and telehealth systems, the use of recordings was the most widely used to

obtain information about the participants’ behavior [92,95,96]. The studies show that all the technologies

are suitable for use as support instruments to work on areas affected by autism. The last parameter

analyzed is related to the cost of developing the tools using the technologies reviewed. The cost is

mainly related to the licenses for the libraries used. This is the case of mixed reality, where there are

free or paid libraries. The dedicated applications and telehealth systems are the least expensive

Int. J. Environ. Res. Public Health 2014, 11 7793

technologies. However, the robots can be considered the most expensive due to the material required

for their manufacture.

Table 5. Comparison of the characteristics of the technologies.







Usability Yes Yes Yes Yes

Invasive systems No No No No

Accesibility Yes Yes Yes Yes

Data acquisition



Mouse/recordings Observation/recordings

Effectiveness Yes Yes Yes Yes

Cost Average




Cost Average

7. Conclusions

The conclusion reached after the analysis carried out in this study is that technology serves as a key

support instrument for people with ASD, their families or professionals treating them.

Technologies can help to work on skills that ASD sufferers may not have developed because they

produce repetitive controlled situations where users can exercise their strengths and weaknesses

time after time, enjoying themselves and not causing tension. Thanks to the repetitive behavior of

technology tools, these people do not expect any improvised social reaction like those that occur in the

real world with social situations involving a large number of stimuli and variants. These environments

produced by researchers are controlled to reduce the participants’ stress.

However, it is essential to ensure that the content fits the children’s ages and to set limits for the

use of these technologies. Just as they can help to practice strengths and improve weaknesses in people

with ASD, they can also create addiction and lead to further isolation.

In addition, mixed reality, robots and dedicated applications achieve interaction with inanimate

objects whose behavior is set and predictable, which makes users feel secure and comfortable working

with them. Although this is an advantage, it has the limitation of not being totally real situations with

the variables involved in interaction in everyday life. For this reason, more research is needed to

demonstrate how training with these technologies improves skills that are transferred to the real world,

thus improving users’ quality of life.

A further limitation in these studies is the fact that the tools are developed for the entire autism

spectrum. In other words, the tools work on all the users’ affected skills in a similar manner, regardless

of the severity of their condition or diagnosis. For this reason, the obtained results in these studies

could be altered, if the cognitive functions or language evolution are analysed. Therefore, they may not

fit each individual’s needs, which could lead to a lack of interest in the system. The capacities these

technologies offer may mean that it would be interesting to develop configurable systems that could be

adapted to each person, thus achieving more efficient tools.

Int. J. Environ. Res. Public Health 2014, 11 7794

Although the studies analyzed do not include robots as tools to work on motor skills, they may be a

good option because of the success shown when working on imitation. In this manner, robots could

reproduce sequences of movements that users could imitate. This would make it possible to work on

both imitation and motor skills.

Another remarkable aspect is that this research consists of pilot studies with a small number of

participants, being mostly children, and further analyses have not been conducted following the

research to check if the participants have maintained the improvement achieved during the tests.

In other words, if the participants have been able to transfer the results achieved to their daily lives.

Following this analysis, it is important to conduct studies which combine various technologies in

the same system to take advantage of each of them and where the main focus area is technology-ASD

person-family/clinician interaction rather than technologies-person interaction. This may make it easier

to transfer the skills they have worked on with the tools to daily life because persons with ASD would

constantly be exposed to real social stimuli in controlled environments.

Technology can therefore give important support in therapy and diagnosis of persons with ASD and

may even help to obtain objective values which enable us to understand autism a bit more and what

people with autism feel in their day to day. This helps professionals to adapt therapies to each person,

and families to work from their homes and gain a better understanding of their children’s behavior

and needs.


This work was partially supported by the Regional Council of Bizkaia, the Basque Country

Department of Education, Universities and Research.

Author Contributions

Nuria Aresti-Bartolome researched the literature and prepared the manuscript. Begonia Garcia-Zapirain

approved and corrected the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.


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