IEEE Systems, Man and Cybernetics Magazine - October 2021 - 7

warrants further research. Recent literature highlights the
importance of online treatments and large adoption of telehealth
solutions [24] across the globe.
Recently, AI has been widely used to investigate autism
with the overall goal of simplifying and speeding up the
diagnostic process as well as making access to early interventions
possible [25], in which supervised machine learning
algorithms have been studied as useful aids to support
decision making in screening [26], diagnosing autism [27],
shorten diagnosing time [28], predicting behavior, and constructing
predictive models [29], [30]. More recently, there
have been a number of developments demonstrating the feasibility
of automated facial behavior analysis systems for
identifying and prediagnosing of autistic individuals. This
research has shown positive advancements in designing the
AI-based platform algorithm to detect and evaluate autistic
patients based on brain activation patterns and mental disorders
[31], faster screening [28], social interaction analysis
[32], [33], facial expression [34], [35], and eye tracing [36].
However, few studies have considered the feasibility of AI in
a natural setting, such as in homes or schools [37]. Effective
treatment of AUIDD relies on efficient and careful behavioral
observation and assessment conducted by board certified
behavioral analysts (BCBA) through assessment, both
direct and indirect. Although current practices for ABA
treatment are highly effective, they are often time-consuming
and are subject to human errors.
Building an explainable AI-augmented learning and
applied behavior analytics (AI-ABA) platform could complement
licensed BAs and therapists who rely on direct
observation of audio-visual cues and other physiological
data available during a session to diagnose and provide
feedback to subjects. AI-ABA could make use of facial
expressions, extremity movements, speech tone, heart
rate, and other available data to build automated pipelines
to detect, diagnose, and alert BAs of during treatment sessions
with clients. Some of the desirable qualities of the AIABA
and learning platform are as follows:
◆ the ability to collect cognitive, perceptive, speech, movement,
and physiological multimodal data of individuals
◆ the ability to define systematic experiments with
dynamic reinforcers in virtual or physical environments
◆ the ability to manage experimental data and results of
recurring experiments of individuals
◆ provide early detection of behavioral changes in children
and explain why, how, and what features were
used for the prediction.
Moreover, AI-ABA platforms can promote self-regulative
behavior using reinforcement-based AR/VR or game environments
and collected multimodal data. It could also promote
creativity and curiosity by designing personalized
dynamic environments that involve physical activities and
speech interactions while completing set tasks. By supporting
games and virtual agents, AI-ABA could promote the
development of spoken communication and social communicative
behavior such that the collaboration and social
skills can be improved by interacting with virtual agents in
social settings using natural language technologies.
Figure 1 illustrates the system architecture of an AIABA
paradigm that complements human intelligence with
AI algorithms. General system architecture of AI-ABA
must consist of 1) multimodal sensing technologies to collect
a variety of sensor data, 2) an AI-augmented treatment
personalizer (ATP) to ingest the sensor data and generate
personalized treatment plans and individualized educational
plans (IEPs) to children, 3) a suite of explainable AI algorithms
to support various requirements of the ATP, and 4)
integration to various presentation formats or front-end
technologies such as AR/VR/MR [38] or tablets [39].
By designing hardware-software systems that cater to
the need of behavior under study, AI-ABA could be used as a
platform for closed-loop real-time multimodal data analysis.
For example, data-repetitive behaviors in individuals with
AUIDD could be collected using cameras or other sensors.
These collected data can be streamed to a computer device
that can host AI algorithms that are optimized to run on limited
hardware resources using quantization operations [40].
The AI predictions could be used to initiate behavioral correction
algorithms that are either deterministic or not. By
exposing the results of behavioral correcting algorithms to
specific web application programming interfaces (APIs),
these results can be viewed on mobile or desktop devices or
visualized on a monitor screen. In the following section, we
summarize a few behavioral challenges of individuals with
AUIDD and how AI-ABA could be a beneficial technology in
augmenting treatment and teaching.
Behavioral Challenges and
Sensing Technologies
Table 1 summarizes some of the common treated behavioral
challenges for children with AUIDD and possible sensor
measurements that could improve data collection while
reducing the physical and mental burden of arduous observational
data collection for BAs during the treatments. Agitation,
aggression, and stereotypy seen in children with
developmental disorders could reoccur due to the process
of reinforcement that parents unknowingly encourage in
the household. Hence, it is very important to track behavioral
changes in multiple settings and environments to
design individualized treatment plans or IEPs for children.
A problem behavior or challenging behavior is any
culturally abnormal behavior that could jeopardize the
physical safety of the individual or others that often
restricts the person from social or communal activities.
Self-harm or self-injurious behavior, harming caregivers,
or general aggression is seen in children with low
frustration tolerance. Throwing items at people, hitting
oneself with objects, banging one's head against an
object, and so on are visible behaviors of aggression
and agitation in children with problem behaviors. Audible
and/or visual behavior such as vocal stereotypy is
commonly identified by parents as their children repeat
October 2021 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE
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IEEE Systems, Man and Cybernetics Magazine - October 2021

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