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MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram.

Kecheng Shi1,2,3, Rui Huang1,2,3, Zhinan Peng1,2,3

  • 1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.

Frontiers in Neuroscience
|December 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel human-exoskeleton interface using upper limb surface electromyography (sEMG) signals to predict lower limb movements in paraplegic patients, achieving high accuracy.

Keywords:
channel synergy-based networkexoskeletonhuman-robot interfacelower limb movement predictionparaplegic patientssurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Neuroscience

Background:

  • Human-robot interfaces (HRI) are crucial for natural human-robot interaction, particularly in exoskeleton applications for movement prediction.
  • Surface electromyography (sEMG) is a mature HRI technology, but lower limb sEMG signals in paraplegic patients are often too weak for reliable exoskeleton control.
  • Existing HRIs rarely consider the synergistic contributions of different sEMG signal channels, limiting their effectiveness.

Purpose of the Study:

  • To develop a novel human-exoskeleton interface for predicting lower limb movements in paraplegic individuals using upper limb sEMG signals.
  • To introduce a channel synergy-based network (MCSNet) for extracting channel contributions and synergy from sEMG data.
  • To validate the proposed interface and network through experiments and analyze the physiological interpretability of extracted features.

Main Methods:

  • Proposed a human-exoskeleton interface utilizing upper limb sEMG signals to infer lower limb movements.
  • Developed and implemented a channel synergy-based network (MCSNet) to analyze the contribution and synergy of sEMG signal channels.
  • Conducted an sEMG data acquisition experiment to evaluate the performance of MCSNet in within-subject and cross-subject scenarios.

Main Results:

  • The proposed MCSNet achieved high movement prediction accuracy: 94.51% within-subject and 80.75% cross-subject.
  • Feature visualization and model ablation analysis confirmed the physiological interpretability of the features extracted by MCSNet.
  • Demonstrated the feasibility of using upper limb sEMG signals for predicting lower limb movements in paraplegic patients.

Conclusions:

  • The developed human-exoskeleton interface effectively predicts lower limb movements in paraplegic patients using upper limb sEMG signals.
  • MCSNet successfully extracts physiologically interpretable features, highlighting the importance of channel contribution and synergy.
  • This approach offers a promising solution for advanced exoskeleton control in individuals with lower limb impairments.