IEEE Systems, Man and Cybernetics Magazine - October 2021 - 10

are used as pointers to understand the hidden
behaviors that might help with a diagnosis. Single-subject
research designs, focused on individual
participants, are often used, even now, to
identify and evaluate interventions due to the heterogeneity
of the AUIDD population. However, the
integration of AI into ABA can allow for collection
of larger data sets and analysis of multimodal
behavioral data, enhancing research standards
and the strength of the research evidence. In reality,
finding interventions that can be considered
evidence-based practices require large cohorts of
subjects and multisession data sets [28]. Hence,
scalable architectures such as AI-ABA should be
explored as a means to collect data and to promote
early detection AUIDD from large subject
populations and predictive models facilitating
responsiveness to intervention.
The Road Ahead
Systems such as AI-ABA could assists practitioners
in making the precise data-based decisions to
increase the quality of individualized intervention for
individuals with AUIDD. This type of system is likely
to have profound effects on the improvement in
treatment efficacy and treatment outcomes, through
a blend of both real-world and virtual elements that
embeds generalization of targeted skills across multiple
environments for the various aspects of an individual's
life. Recent studies illustrate the importance
of active involvement of children in the intervention
process [42], [43]. Additionally, providing a learning
environment with effective interaction and communication
is another important factor that should be
taken into account [44], [45]. Since AI-ABA provides
the optimal use of delivering ABA services for children
with developmental and cognitive needs, the
system could easily be integrated to across environments,
such as schools and home, to deliver a more
comprehensive treatment program. This smart and
connected health via behavioral sensing would not
only be highly desirable for existing outpatient clinics
but also can be integrated into telehealth platforms
to facilitate service access to those living in
rural and deprived services areas and can provide a
level of system resiliency during periods where
human interaction is limited, such as the ongoing
COVID-19 pandemic. In addition, applying homebased
ABA can help ensure parents involvement
with their child training alongside other stakeholders
(e.g., teachers and caregivers) to create a more comprehensive
support network for service delivery.
AI-ABA will help researchers to focus on the precision
within intervention research, reinforcers
effectiveness, and individualized treatment models
while augmenting part of the data collection and
10 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE October 2021
Brain Activation Map
DL Model
EEG Data
64
Frontal Left
Central
Temporal Left
Parietal
Occipital
(a)
AU 10: Upper Lip Raiser
Facial Muscle Movement
Analyzer
Action Units Segregated
by Facial Region
0.4
0.3
0.2
0.1
0 500 1,000
Trial 1 (ms)
(b)
Figure 3. The attention span of children can be studied by capturing EEG signals or eye-tracking information, whereas vocal stereotypy can be predicted by
studying the facial muscle movement patterns over time. In (a), we illustrate a DL model generating a prediction and corresponding brain activation maps
highlighting the most attributing region of the brain. Vocal stereotypy (b) can be detected by observing the repetitive motion in the lower facial muscle
groups using a facial muscle movement analyzer.
0.4
0.5
0.3
0.2
0.1
1,500 0 500 1,000
Trial 2 (ms)
1,500
Vocal stereotypy can be detected
by studying the repeating lower
facial muscle movements.
Temporal
Right
Start of Trail
End of Trail
Frontal Right
Attention span can be
predicted using
EEG signals.
IEEE Systems, Man and Cybernetics Magazine - October 2021

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