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\author[1,2]{Tianyi Wang}%
\author[1]{Takuya Sakamoto}%
\author[1]{Yu Oshima}%
\author[1]{Itsuki Iwata}%
\author[1]{Masaya Kato}%
\author[1]{Haruto Kobayashi}%
\author[3]{Manabu Wakuta}%
\author[4]{Masako Myowa}%
\author[5]{Tokomo Nishimura}%
\author[5]{Atsushi Senju}%
\affil[1]{Department of Electrical Engineering, Graduate School of Engineering, Kyoto University}%
\affil[2]{Institute for Multidisciplinary Sciences, Yokohama National University}%
\affil[3]{Research Department, Institute of Child Developmental Science Research}%
\affil[4]{Division of Interdisciplinary Studies in Education, Graduate School of Education, Kyoto University}%
\affil[5]{Research Center for Child Mental Development, Hamamatsu University School of Medicine}%


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  \date{June 15, 2024}


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\begin{abstract}
Traditional assessments of children's health and behavioral issues
primarily rely on subjective evaluation by adult raters, which imposes
major costs in time and human resource to the school system. This pilot
study investigates the utilization of millimeter-wave radar coupled with
machine learning for the objective and semi-automatic detection and
classification of children's activity levels, defined as restlessness,
within a real classroom environment. Two objectives are pursued:
confirming the feasibility of restlessness detection using
millimeter-wave radar and proposing an algorithm for restlessness
classification through machine learning. The experiment involves a
nine-day observational study, using two radar systems to monitor the
activities of 14 children in a primary school. Radar data analysis
involves the extraction of distinctive features for restlessness
detection and classification. Results indicate the successful detection
of restlessness using millimeter-wave radar, demonstrating its potential
to capture nuanced body movements in a privacy-protected manner. Machine
learning models trained on radar data achieve a classification accuracy
of 100\%, outperforming other methods in terms of non-invasiveness, lack
of body restraint, multi-target applications, and privacy protection.
The study's contributions extend to children, parents, and educational
practitioners, emphasizing non-invasiveness, privacy protection, and
evidence-based support. Despite limitations such as a short monitoring
duration and a small sample size, this pilot study lays the foundation
for future research in non-invasive restlessness detection using
non-contact monitoring technologies. The integration of millimeter-wave
radar and machine learning offers a promising avenue for efficient and
ethical trait assessments in real-world educational environments,
contributing to the advancement of child psychology and education. This
work supports efforts for non-contact monitoring of children's activity
holding promise such as non-invasive, privacy protection, multi-targets,
objective evaluation, and computer-aided screening.%
\end{abstract}%



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