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\begin{document}

\title{AIoT-enhanced health management system using soft and stretchable
triboelectric sensors for human behavior monitoring}



\author[1]{Liang  Xu}%
\author[1]{Songyi  Zhong}%
\author[1]{Tao  Yue}%
\author[2]{Zixuan Zhang}%
\author[1]{Xiao Lu}%
\author[1]{Yangqiao  Lin}%
\author[1]{Long  Li}%
\author[1]{Yingzhong  Tian}%
\author[3]{Tao  Jin}%
\author[3]{Quan  Zhang}%
\author[2]{Chengkuo Lee}%
\affil[1]{Shanghai University}%
\affil[2]{National University of Singapore}%
\affil[3]{Shanghai University School of Mechatronic Engineering and Automation}%


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  \date{May 19, 2024}


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\begin{abstract}
Sedentary, inadequate sleep and exercise can affect human health.
Artificial intelligence (AI) and Internet of thing (IoT) creates the
Artificial Intelligence of Things (AIoT), providing the possibility to
solve these problems. This paper presents a novel approach to monitor
various human behaviors for AIoT-based health management using
triboelectric nanogenerator (TENG) sensors. The insole with solely one
TENG sensor, creating a most simplified system that utilizes machine
learning (ML) for personalized motion monitoring, encompassing identity
recognition and gait classification. A cushion with 12 TENG sensors
achieves real-time identity and sitting posture recognition with
accuracy rates of 98.86\% and 98.40\%, respectively, effectively
correcting sedentary behavior. Similarly, a smart pillow, equipped with
15 sensory channels, detects head movements during sleep, identifying 8
sleep patterns with 96.25\% accuracy. Ultimately, constructing an
AIoT-based health management system to analyzes these data, displaying
health status through human-machine interfaces, offering the potential
to help individuals maintain good health.%
\end{abstract}%



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\verb`Cover Letter.docx`  available at \url{https://authorea.com/users/761721/articles/737852-aiot-enhanced-health-management-system-using-soft-and-stretchable-triboelectric-sensors-for-human-behavior-monitoring}

\textbf{Hosted file}

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