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A long short-term memory modeling-based compensation method for muscle synergy.

Zhengye Pan1, Lushuai Liu1, Xingman Li1

  • 1College of Physical Education and Sports, Beijing Normal University, Beijing, China.

Medical Engineering & Physics
|October 14, 2023
PubMed
Summary

This study introduces a novel muscle synergy compensation method to improve kinematic predictions during running. Half-compensation enhances predictive accuracy with minimal disruption to original muscle synergy patterns.

Keywords:
Change of directionLong short-term memoryMuscle synergyUncontrolled manifold

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Area of Science:

  • Biomechanics
  • Neuroscience
  • Robotics

Background:

  • Muscle synergy, encompassing temporal and spatial muscle activation patterns, is crucial for predicting movement kinematics.
  • Existing methods often show discrepancies between predicted and actual movement performance, necessitating improved predictive accuracy.

Purpose of the Study:

  • To propose and validate a new method for compensating muscle synergy to enhance the accuracy of kinematic characteristic predictions.
  • To investigate the effectiveness of different compensation strategies (all-compensation vs. half-compensation) in predicting pelvic center of mass movement during running changes of direction.

Main Methods:

  • Non-negative matrix factorization (NMF) was employed to extract muscle synergies during changes of direction at various angles.
  • Long and short-term memory (LSTM) neural networks established a non-linear association between synergy and pelvic center of mass height.
  • Backpropagation neural networks assessed the accuracy of compensated synergies in predicting pelvic center of mass displacement.

Main Results:

  • Compensated muscle synergy significantly improved the accuracy of predicting pelvic center of mass displacement (R² , p < 0.05).
  • All-compensation resulted in significant differences from actual performance in end-swing, potentially due to individual variability.
  • Half-compensation showed no significant difference from actual performance, with less disruption to original synergy and smaller error increase with angle.

Conclusions:

  • The proposed muscle synergy compensation method, particularly half-compensation, enhances predictive accuracy for kinematic characteristics.
  • Half-compensation offers a viable approach by improving predictive accuracy while minimizing alterations to the original muscle synergy, making it robust across different movement angles.