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Related Experiment Video

Updated: Jul 19, 2025

Evaluating Postural Control and Lower-extremity Muscle Activation in Individuals with Chronic Ankle Instability
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Mechanomyography Signal Pattern Recognition of Knee and Ankle Movements Using Swarm Intelligence Algorithm-Based

Yue Zhang1, Maoxun Sun2, Chunming Xia3

  • 1School of Mechanical Engineering, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary

This study introduces Chameleon Swarm Algorithm (CSA) and Grasshopper Optimization Algorithm (GOA) for recognizing lower-limb movements using mechanomyography (MMG) signals in wearable devices. CSA achieved higher accuracy, while GOA was faster with fewer features.

Keywords:
chameleon swarm algorithmfeature selectiongrasshopper optimization algorithmmechanomyographypattern recognition

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Signal Processing

Background:

  • Mechanomyography (MMG) signal pattern recognition is crucial for developing effective wearable rehabilitation-training devices.
  • Accurate identification of lower-limb movements, such as knee and ankle actions, is essential for personalized rehabilitation.
  • Existing methods may lack efficiency or optimal feature selection for complex movement patterns.

Purpose of the Study:

  • To propose and evaluate novel MMG feature selection methods using Chameleon Swarm Algorithm (CSA) and Grasshopper Optimization Algorithm (GOA).
  • To assess the performance of CSA and GOA in recognizing knee and ankle movements in sitting and standing positions.
  • To compare the trade-offs between classification accuracy, feature selection, and computational time of the two algorithms.

Main Methods:

  • Designed and utilized wireless multichannel MMG acquisition systems to collect signals from thigh muscle sites.
  • Implemented CSA and GOA for feature selection from MMG data.
  • Analyzed the impact of varying threshold values on classification accuracy.
  • Evaluated recognition rates for sitting and standing positions.

Main Results:

  • Both CSA and GOA achieved high recognition rates after redundant information elimination.
  • CSA demonstrated robust performance with fluctuating recognition rates up to 88.17% (sitting) and 90.07% (standing) as thresholds increased.
  • GOA showed a dramatic drop in recognition rates with increasing thresholds but consumed less time and selected fewer features.

Conclusions:

  • CSA offers superior recognition rates for knee and ankle movements compared to GOA.
  • GOA provides a more time-efficient feature selection process with a reduced feature set.
  • The choice between CSA and GOA depends on the specific requirements for accuracy versus efficiency in MMG-based rehabilitation systems.