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Upper Extremity Muscle Activation Pattern Prediction Through Synergy Extrapolation and Electromyography-Driven

Shadman Tahmid1, Josep M Font-Llagunes2, James Yang1

  • 1Human-Centric Design Research Lab, Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409.

Journal of Biomechanical Engineering
|October 30, 2023
PubMed
Summary

This study presents a new method to predict deep muscle activations using surface electromyography (EMG) and muscle synergies. This approach improves muscle force and joint torque estimations for neuromuscular disease patients.

Keywords:
EMG-driven modelmuscle activationmuscle synergyupper extremity

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Area of Science:

  • Biomechanics
  • Neuromuscular Physiology
  • Rehabilitation Engineering

Background:

  • Accurate measurement of muscle force is crucial for understanding neuromuscular diseases but is challenging.
  • Surface electromyography (EMG) can measure superficial muscle activation, but deep muscle activation is difficult to assess non-invasively.
  • Existing EMG-driven models using only surface electrodes may underestimate net joint torque.

Purpose of the Study:

  • To develop and validate a novel methodology for predicting deep muscle activations in the upper extremity.
  • To enhance the accuracy of EMG-driven musculoskeletal models by incorporating predictions of unmeasured muscle activations.
  • To personalize musculoskeletal models with subject-specific muscle-tendon parameters.

Main Methods:

  • A hybrid approach combining an EMG-driven musculoskeletal model with muscle synergies was employed.
  • The method iteratively predicts individual unmeasured muscle activations by tracking inverse dynamics joint moments.
  • Muscle synergies were used to determine synergy vector weights for predicting activations of shoulder and elbow muscles.

Main Results:

  • The methodology accurately predicted single unmeasured muscle activations, achieving high correlation coefficients (R=0.99 for elbow, R=0.92 for shoulder).
  • For complex five degree-of-freedom (DoF) tasks, prediction accuracy reached R=0.71.
  • Personalization of muscle-tendon parameters (optimal fiber length, tendon slack length, maximum isometric force) was performed.

Conclusions:

  • The proposed methodology effectively predicts deep muscle activations non-invasively, overcoming limitations of surface EMG alone.
  • This approach offers a promising tool for improving the assessment of muscle function in individuals with neuromuscular disorders.
  • The method's accuracy across various upper extremity tasks supports its potential application in rehabilitation and clinical settings.