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Related Experiment Video

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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A Smartphone-Based sEMG Signal Analysis System for Human Action Recognition.

Shixin Yu1, Hang Zhan1, Xingwang Lian1

  • 1College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.

Biosensors
|August 25, 2023
PubMed
Summary

This study introduces a portable system for real-time analysis of surface electromyography (sEMG) signals during lower-limb rehabilitation. The system achieves over 97% accuracy in recognizing patient movements, aiding clinical evaluation.

Keywords:
HARdeep learningrehabilitationsEMGsmartphone

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Human Action Recognition

Background:

  • Surface electromyography (sEMG) provides objective data for lower-limb rehabilitation.
  • Traditional sEMG analysis can be time-consuming and inconvenient.
  • Need for accessible, real-time monitoring solutions in daily rehabilitation scenarios.

Purpose of the Study:

  • To develop a portable sEMG acquisition device and mobile application for daily use.
  • To enable real-time monitoring, analysis, and human action recognition (HAR) of lower-limb movements.
  • To create a reliable system for clinical evaluation in lower-limb rehabilitation.

Main Methods:

  • Development of a portable sEMG signal acquisition device and a companion mobile application.
  • Collection of sEMG data for six distinct lower-limb rehabilitation exercises.
  • Training a convolutional neural network (CNN) model using sEMG segments and action labels.

Main Results:

  • The developed mobile application offers real-time sEMG data plotting, filtering, storage, and action recognition.
  • The CNN model achieved high-precision human lower-limb action recognition, with a maximum accuracy of 97.96%.
  • All tested lower-limb actions were recognized with over 97% accuracy.

Conclusions:

  • The smartphone-based sEMG analysis system is effective for real-time monitoring and HAR in lower-limb rehabilitation.
  • The system offers reliable data for objective clinical evaluation, improving rehabilitation efficiency.
  • This technology facilitates convenient and accurate assessment of patient actions in daily settings.