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AI-Enabled Sensing Wristband for Student Behavior Detection.

Hexiang Zhang1,2, Jiaoyi Wu2,3, Rui Zou2,4

  • 1Yibin Research Institute, Southwest Jiaotong University, Yibin 64000, P. R. China.

ACS Applied Materials & Interfaces
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered wristband using electromagnetic and triboelectric generators for energy harvesting. The device achieves 98.75% accuracy in detecting student behaviors, paving the way for smart healthcare and environments.

Keywords:
AI-enabled wristbandbehaviors detectiondeep learningdigital twinsenergy harvestingtriboelectric nanogenerator

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Area of Science:

  • Wearable technology
  • Artificial Intelligence (AI)
  • Internet of Things (IoT)

Background:

  • Advancements in AI and IoT are driving the development of wearable sensors for smart healthcare and behavior detection.
  • Existing technologies require further integration for comprehensive sensing and energy harvesting capabilities.

Purpose of the Study:

  • To propose an AI-enabled sensing wristband integrating electromagnetic and triboelectric nanogenerator modules for energy harvesting and user behavior detection.
  • To evaluate the performance of the proposed multiscale convolutional channel attention residual network (MCRnet) for accurate behavior classification.
  • To demonstrate a smart classroom application utilizing the sensing wristband with digital twin and 5G technologies.

Main Methods:

  • Development of an AI-enabled sensing wristband with electromagnetic generator (EMG), triboelectric nanogenerator (TENG), and user behavior detection (UBD) modules.
  • Simulation of the EMG module using finite element method magnetics (FEMM) and experimental validation through vibration and human motion tests.
  • Collection of activity signals from 20 individuals across eight defined student behaviors for dataset creation and model training.

Main Results:

  • The EMG module successfully generated an output power of 2.42 mW from wrist swinging, sufficient for system operation.
  • The MCRnet achieved a student behavior detection success rate of 98.75% after parameter optimization.
  • Successful demonstration of a smart classroom application integrating digital twin and 5G communication.

Conclusions:

  • The proposed AI-enabled sensing wristband offers a viable solution for energy harvesting and accurate user behavior detection.
  • The MCRnet demonstrates high efficacy in classifying complex human activities from sensor data.
  • The integration with digital twin and 5G technologies highlights the wristband's potential in future intelligent living environments.