JoVE - Journal of Visualized Experiments
JoVE Visualize
Contact Us

Research on the electromyography-based pattern recognition for inter-limb coordination in human crawling motion

  • 0School of Information Science and Technology, University of Science and Technology of China (USTC), Hefei, Anhui, China.
Frontiers in Neuroscience +

|

March 29, 2024

|

March 29, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces electromyography (EMG) to analyze crawling coordination, successfully classifying eight inter-limb coordination modes (ILCMs). This EMG-based method offers a feasible approach for clinical crawling motion analysis.

Area Of Science

  • Biomechanics
  • Neuroscience
  • Rehabilitation Engineering

Background

  • Clinical analysis of human crawling motion lacks objective and feasible methods.
  • Understanding inter-limb coordination during crawling is crucial for functional assessment.

Purpose Of The Study

  • To develop and validate an electromyography (EMG)-based method for analyzing inter-limb coordination modes (ILCMs) during human crawling.
  • To assess the feasibility of motion intention recognition technology for clinical applications in crawling analysis.

Main Methods

  • Defined eight distinct inter-limb coordination modes (ILCMs).
  • Collected EMG signals from 30 muscles and pressure data from the left palm during hands-knees crawling at various speeds.
  • Employed bidirectional long short-term memory (BiLSTM), support vector machine (SVM), and k-nearest neighbor (KNN) classifiers for pattern recognition.

Main Results

  • EMG-based pattern recognition achieved high accuracy in classifying the eight ILCMs.
  • The study confirmed the feasibility of an EMG-based crawling motion analysis method for clinical use.
  • Analysis of self-selected ILCMs at different speeds provided insights into coordination strategies.

Conclusions

  • EMG-based motion intention recognition is a viable technology for analyzing crawling inter-limb coordination.
  • The developed method holds potential for evaluating crawling function, understanding abnormal control, and designing rehabilitation robots.

Keywords: