IEEE Systems, Man and Cybernetics Magazine - October 2021 - 11

Wearable Physiological
Data Sensor
Data collected at 1 Hz using
devices such as Empatica E4
can be used to for sleep
staging and sleep quality
studies.
110
100
90
80
70
60
50
110
100
90
80
70
60
50
Sleep Hours
Figure 4. Wearable physiological sensors such as Empatica E4 can be
used to collect heart rate data and study variability to carry out sleep
staging and sleep quality analysis before and after therapy sessions.
Here, the red dotted lines indicate intermittent waking-up states.
analysis to AI algorithms. For example, a system capable
of handling multiple sensory inputs for data capture could
in a plug-and-play manner collect data using specific APIs
and process them using existing machine learning algorithms.
The processed data and results could be used to
dynamically influence the virtual environments, learning
structures, the precision of treatment plans, effectiveness,
and the incorporation of AR or VR digital platforms to promote
greater access to intervention and generalization of
treatment effects. AR and VR feedback loops could
increase learning engagement and raise comprehension of
topics, provide interaction, improve communication, trigger
imagination, and enhance problem-solving skills, especially
when involving spatial skills [38]. A combination of
AR and VR technologies with other invasive and noninvasive
data collection systems could collect both physiological
and behavioral data to study temporal dynamics of
behavior in children. Additionally, this could enable justin-time
adaptive interventions by collecting precise data
and provide a repository of prior behavior of each client.
Acknowledgments
This project was funded partly by both the Open Cloud
Institute at the University of Texas at San Antonio (UTSA)
and the UTSA Brain Health Consortium and Office of the
Vice President for Research, Economic Development, and
Knowledge Enterprise. Arun Das and Shadi Ghafgazhi
contributed equally. We gratefully acknowledge the use of
the services of Jetstream cloud.
About the Authors
Shadi Ghafghazi (sh.ghafghazi@gmail.com) earned her
master's degree in information science and knowledge studies
from Shahid Beheshti University and her bachelor's
degree in library and information science from the University
of Tehran, Iran. She is a senior lecturer at the
University of Applied Science and Technology,
Hamedan, 6513835317, Iran, where she is teaching
special needs education courses.
Amarie Carnett (amarie.carnett@vuw.ac
.nz) is a senior lecturer with the College of Education,
Victoria University of Wellington, 6012,
New Zealand, and a research faculty member at
the University of Texas at San Antonio, Texas,
78249, USA. She is a doctorate-level board-certified
behavior analyst.
Leslie Neely (leslie.neely@utsa.edu) is an
associate professor in the College of Education
and Human Development, University of Texas at
San Antonio, Texas, 78249, USA. and director of
the Child and Adolescent Policy and Research
Institute, San Antonio, Texas, 78249, USA. She is
a doctorate-level board-certified behavior analyst.
Arun Das (arun.das@utsa.edu) earned his
M.S. degree in computer engineering from the
University of Texas at San Antonio and his B.Tech.
degree from Cochin University of Science and Technology. He
is a Ph.D. candidate at the University of Texas at San Antonio,
Texas, 78249, USA. His research focuses on explainable AI and
self-supervised algorithms for health care and neuroscience
domains. He is a Graduate Student Member of the IEEE Lone
Star section and the IEEE Eta Kappa Nu honor society.
Paul Rad (paul.rad@utsa.edu) is an associate professor in
the Department of Computer Science, University of Texas at
San Antonio, Texas, 78249, USA. He is a senior member of the
National Academy of Inventors and a Senior Member of IEEE.
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October 2021 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 11
Heart Rate
Heart Rate
Wakeful State

IEEE Systems, Man and Cybernetics Magazine - October 2021

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