IEEE Systems, Man and Cybernetics Magazine - October 2021 - 6

Several studies have shown promising results in improving
the learning performance and boosting motivation to
learn using graphical contents and interactions [5]. Teaching
strategies and interventions that utilize digital games in
mobile devices or tablets have also shown promise for
incorporating behaviors management techniques into
games. Restrictive and repetitive behaviors and interests
(RRIBs) that might occur while playing games could be
monitored and treated using embedded automated redirection
to other games or levels to prevent interfering behaviors
that prevent access to learning opportunities and help
promote calmness [6]. However, considering the range of
functioning for individuals with cognitive disabilities, further
studies involving precision of treatment options to
ensure individualization are needed as well as replication of
these results across large cohorts of participants [7].
Role of AR and VR
Emerging technologies such as AR, including VR and
mixed reality (MR), are at the forefront of recent technology-embedded
practices that overlays reality and supplies
additional layers to augment the perception of users [8] as
well as enabling real-time interaction of real and virtual
objects [9]. Recent interest in using AR and VR technologies
to aid adults and children with autism spectrum disorder
(ASD) provides additional sensory information such as
eye-tracking as well as a virtual platform to continuously
interact with people and environment around them in a
controlled setting while collecting data for future analysis.
Safe and side-effect-free technologies are changing how
AR/VR platforms are being found to be beneficial for
improving soft skills, behaviors, and improving emotional
skills [10]. However, the importance of personalized services
to provide augmentation for individual learners has yet
to be researched at large. Thus, the need for personalized
adaptive learning paradigms is required to improve engagement,
autonomy, and to promote individualized preferences
for children with cognitive disorders. Data-driven algorithms
could make use of these digital technologies to
improve data collection while reducing the demands placed
on behavioral analysts and educators and the time required
to collect and label these behavioral data.
Artificially Intelligent Methods
in Behavioral Health
Recent advancements in AI have enabled real-time human
action performance [11], facial behavioral analysis [12],
speech analysis [13], speech disfluency detection [14], stereotypical
motor movement from sensory data [15], and many
more. Published research from the past five years shows the
use of a wide variety of sensory inputs to predict human
behavior, diseases, and cognitive states using AI methods,
especially deep learning (DL). Electroencephalograms (EEGs)
have been used extensively to study the internal brain states
by recording the electrical activity of brain waves for health
monitoring [16], predicting diseases such as Parkinson's [17]
6
IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE October 2021
and assessing emotional disorders [18]. Even though using
EEGs to study autism could have contradictions based on the
experimental conditions during EEG registration among subjects,
age differences, and diversity of subjects, the abnormal
EEG laterization in subjects with ASD can be leveraged to
build AI models to predict traits of autism [19].
Prior research using DL algorithms illustrate the successful
use of facial videos collected using cameras to estimate
the attention and engagement of children with
developmental disorders [20]. Similarly, inertial measurement
unit sensors that rely on accelerometer, gyroscope,
and magnetometers that can collect information about the
frequency, intensity, and duration of physical activities
have shown to detect stereotypical movements in ASD children
[15]. As our children rely more on digital devices such
as iPads and mobile phones to read, learn, and interact
from an early age [21], it is only natural to research in facilitating
access to individualized digital content through a
variety of interfaces (such as games, interactive lessons,
maps, and more) to cater for the exceptional learners.
Challenges of AI in Behavioral Health
Despite the impressive role of AI in behavioral health, there
are two key open challenges that limits its use in clinical decision
making: 1) limited amount of labeled data to train AI
algorithms and 2) black-box nature of deep neural network
models. There are two potential solutions to these problems.
First, self-supervised representation learning has been recently
used to learn meaningful dense representations from small
amount of data. Also, reinforcement learning paradigms can
learn to optimize based on an exploration-exploitation paradigm
on any defined environment. Second, explainable AI
methods can be used to improve the transparency and trust
of decision making by generating meta-information to
describe " why " and " how " a decision was made while suggesting
" what " features influenced the decision the most [24].
Personalized Explainable AI to
Improve ABA and Treatment
ABA is an effective and evidence-based treatment to address
individual needs for children with developmental and intellectual
disabilities. Additionally, ABA treatment programs have
been successfully implemented in different settings such as
home, communities, school, and other educational centers.
The beauty of ABA is that it focuses on data-based decision
making, which allows the clinician to tailor interventions and
personalize treatment options to an individual's unique situation
and need. However, as previously discussed, the analysis
process is time-consuming and has limitations due to its reliance
on humans as the data collectors and pattern analyzers,
which is subjective given the reliance on professional judgment.
These limitations can also be confounded by the everincreasing
demands placed on BAs and educators, such as
increased caseloads, time demands, and limited resources.
Augmenting and complementing the systematic evaluation of
subject's data using intelligent algorithms is an avenue that

IEEE Systems, Man and Cybernetics Magazine - October 2021

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