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Updated: May 28, 2026

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sEMG-Based Motion Intention Recognition for Interactive Upper Limb Nursing Assistance.

Zekun Peng1, Yongfei Feng2, Liangda Wu2

  • 1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a robust framework using surface electromyography (sEMG) for recognizing upper-limb motion intentions. The system achieved high accuracy, showing promise for real-time human-machine interaction in assistive devices.

Keywords:
feature fusioninteractive nursing assistancemotion intention recognitionsurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Human-Machine Interaction

Background:

  • Surface electromyography (sEMG) offers non-invasive neuromuscular activity measurement.
  • Reliable, real-time motion intention recognition is crucial for advanced human-machine interfaces, particularly in upper-limb prosthetics and exoskeletons.
  • Current sEMG decoding methods face challenges in accuracy and real-time performance for interactive assistive applications.

Purpose of the Study:

  • To present a structured framework for sEMG-based motion intention recognition in upper-limb assistance.
  • To evaluate the efficacy of time-domain features and ensemble learning for decoding sEMG signals.
  • To validate the proposed framework's performance in both offline and online interactive settings.

Main Methods:

  • Acquisition of multi-channel sEMG signals during four distinct upper-limb motions.
  • Standardized preprocessing, including denoising and segmentation, followed by extraction of eight time-domain features.
  • Systematic feature subset selection and benchmarking of five supervised learning classifiers, with Random Forest optimized using K-fold cross-validation.

Main Results:

  • A compact subset of time-domain features was identified as optimal for motion discrimination.
  • The Random Forest classifier, optimized through K-fold cross-validation, demonstrated superior performance.
  • The framework achieved an average intra-subject accuracy of 95.23% and 95.72% in online validation.

Conclusions:

  • Time-domain feature fusion combined with ensemble learning provides robust and efficient motion discrimination from sEMG signals.
  • The developed framework shows significant potential for real-time applications in upper-limb assistance, rehabilitation, and advanced human-machine interaction.
  • This approach offers a reliable method for decoding user intentions, paving the way for more intuitive and responsive assistive technologies.