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Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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Instantaneous recognition method for lower limb continuous motion based on onset-window surface electromyography

Xiaohui Li1,2,3, Hao Zhou1,3, Xueyan Lyu1,2,3

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.

Journal of Neural Engineering
|August 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for recognizing lower limb motion using surface electromyography (sEMG) signals, significantly reducing delay and improving accuracy for human-robot collaboration in rehabilitation. The approach enables faster, more precise control of robotic devices.

Keywords:
class-balanced methodinstantaneous recognitionlower limb continuous motiononset-window sEMG data

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Human-robot collaboration in lower-limb rehabilitation demands high accuracy and real-time responsiveness for motion intention recognition.
  • Surface electromyography (sEMG) is crucial for precise lower limb motion recognition.
  • Achieving low-delay recognition in continuous motion while maintaining accuracy is a significant challenge for robotic applications.

Purpose of the Study:

  • To present an innovative recognition method for lower limb continuous motion.
  • To investigate the instantaneous recognition network (IRN) and continuous recognition (CR) model for improved human-robot synchronization.
  • To address the challenge of low-delay, high-accuracy motion recognition in lower limb rehabilitation robots.

Main Methods:

  • Developed an instantaneous recognition network (IRN) and a continuous recognition (CR) model.
  • Optimized the onset-window surface electromyography (sEMG) data length to 210.
  • Implemented a class-balanced method to enhance motion recognition accuracy.
  • Validated the CR model across seven diverse scenarios of daily continuous movements.

Main Results:

  • The IRN reduced time delay from 300-350 ms to 60 ms.
  • The class-balanced method improved motion recognition accuracy by 4.83% within the onset window.
  • The CR model achieved an average accuracy of 96.31% across seven scenarios.

Conclusions:

  • The proposed instantaneous recognition method significantly enhances performance in lower limb continuous motion recognition.
  • This study offers an innovative approach for improving human-robot synchronization in rehabilitation.
  • The findings pave the way for more effective deployment and widespread application of rehabilitation robots.