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This study introduces a novel myoelectric control method using neural data and musculoskeletal models. It accurately estimates muscle activity and wrist movement, outperforming current artificial neural network approaches for prosthetics and rehabilitation.

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Technology

Background:

  • Myoelectric control is crucial for prosthetics and rehabilitation.
  • Current methods often rely on artificial neural networks for signal processing.
  • Accurate estimation of muscle activity and joint kinematics is essential for effective control.

Purpose of the Study:

  • To develop and validate a myoelectric control method integrating neural data regression and musculoskeletal modeling.
  • To improve the accuracy of estimating muscle excitations and joint kinematics from high-density surface electromyogram (HD-EMG) signals.
  • To assess the potential of this data-driven model-based approach in prosthetics and rehabilitation.

Main Methods:

  • Decoding motor neuron discharge timings from HD-EMG decomposition to estimate muscle excitations.
  • Utilizing forward dynamics of a musculoskeletal model to map muscle excitations to wrist joint kinematics.
  • Comparing the proposed method's offline tracking performance against artificial neural network-based regression methods.

Main Results:

  • The proposed method demonstrated superior offline tracking performance compared to state-of-the-art artificial neural network methods in two amputee subjects.
  • The method achieved better performance in four out of six intact-bodied subjects.
  • The approach successfully estimated biomechanical variables in a feed-forward manner, relevant for rehabilitation and training.

Conclusions:

  • Integrating neural data regression with musculoskeletal modeling offers a promising avenue for advanced myoelectric control.
  • This data-driven, model-based approach provides a robust framework for understanding muscle excitation to joint function transformations.
  • The method holds significant potential for enhancing prosthetic limb control and aiding in rehabilitation and training processes.