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AI-Assisted Self-Powered Wearable Dual-Mode Sensor With TENG and Stretchable Optical Fiber for Neurological Disorder

Tianliang Li1, Han Liu1, Guoxu Liu2,3

  • 1School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
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Summary

This study introduces a self-powered wearable sensor for neurological disorders. It accurately distinguishes Parkinson's disease and stroke, aiding in rehabilitation progress evaluation.

Keywords:
AI‐assisted diagnosisdual‐mode sensorself‐poweredtriboelectric nanogenerator

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Wearable Technology

Background:

  • Current wearable sensors for lower-limb dysfunction in neurological disorders are limited by single-mode sensing, reliance on external power, and restricted diagnostic abilities.
  • Effective management of conditions like Parkinson's disease and stroke requires advanced monitoring and diagnostic tools.

Purpose of the Study:

  • To develop a wireless wearable dual-mode sensor (WDMS) that integrates muscle signal monitoring and plantar pressure sensing with energy harvesting capabilities.
  • To utilize an embedded AI model for accurate diagnosis of neurological disorders and quantitative evaluation of rehabilitation progress.
  • To overcome the limitations of existing wearable systems by providing a self-powered, multimodal sensing solution.

Main Methods:

  • Integration of three flexible optical strain sensors (PFOS) for muscle monitoring with a contact-separation mode triboelectric nanogenerator (CS-TENG) for plantar pressure sensing and energy harvesting.
  • Development and implementation of a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model for data analysis.
  • Clinical validation using gait data from 60 individuals with neurological disorders.

Main Results:

  • The WDMS successfully integrated muscle signal monitoring, plantar pressure sensing, and self-powered operation, eliminating external power dependence.
  • The embedded CNN-LSTM model achieved 94.23% accuracy in distinguishing between Parkinson's disease and stroke patients.
  • The system quantitatively evaluated rehabilitation progress following therapeutic interventions.

Conclusions:

  • The developed WDMS offers a self-sufficient and scalable solution for intelligent diagnostic support in managing lower-limb dysfunction in neurological disorders.
  • This technology advances beyond passive monitoring, enabling transformative home-based management and personalized rehabilitation.
  • The synergistic combination of multimodal sensing, AI-driven analysis, and clinical validation represents a significant step forward in wearable healthcare technology.