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Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models.

Shing-Hong Liu1, Chi-En Ting1, Jia-Jung Wang2

  • 1Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
Summary

This study estimates gait parameters using only lower limb muscle activity (sEMGs), achieving high accuracy for 14 parameters. This offers a more comprehensive gait analysis without complex equipment.

Keywords:
GaitUp Physilog® wearable inertial sensorsXGBoostdecision treegait parametersmachine learningrandom forestsurface electromyogram

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Area of Science:

  • Biomechanics
  • Kinesiology
  • Biomedical Engineering

Background:

  • Gait analysis objectively assesses gait improvement procedures, aiding physicians in understanding gait problems, etiology, and treatment.
  • Traditional gait analysis measures kinematics (temporal, spatial parameters) but lacks skeletal muscle activity information.
  • Wearable inertial sensors (e.g., GaitUp Physilog®) measure gait parameters but not muscle activity.

Purpose of the Study:

  • To estimate gait parameters using surface electromyograms (sEMGs) from lower limb muscles.
  • To develop a method for comprehensive gait analysis incorporating muscle activity.

Main Methods:

  • Measured sEMGs from vastus lateralis and gastrocnemius muscles using a self-made wireless device.
  • Recruited twenty young female subjects with low skeletal muscle index (SMI).
  • Utilized three machine learning models (Random Forest, Decision Tree, XGBoost) to estimate 23 gait parameters from 4 sEMG parameters.
  • Gait parameters were measured using GaitUp Physilog® wearable inertial sensors.

Main Results:

  • Successfully estimated 14 gait parameters with correlation coefficients above 0.800.
  • Demonstrated the feasibility of using sEMG data to infer kinematic gait parameters.

Conclusions:

  • This study represents a significant step towards more comprehensive gait analysis using only sEMGs.
  • The findings suggest a potential for simpler, more informative gait assessment tools.