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Movement joints in buildings are essential design elements that accommodate inevitable motions caused by various factors such as temperature changes, moisture content variations, and structural deflections. These motions, if not considered in design and construction, can lead to unsightly or dangerous damage. Movement joints are incorporated in different forms to manage these stresses and allow materials to move without causing distress.
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Related Experiment Video

Updated: May 28, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
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Improving movement decoding performance under joint constraints based on a neural-driven musculoskeletal model.

Lizhi Pan1,2, Xingyu Yan1,2, Shizhuo Yue1,2

  • 1The Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.

Medical & Biological Engineering & Computing
|February 11, 2025
PubMed
Summary
This summary is machine-generated.

A new neural-driven musculoskeletal model (N-DMM) improves movement decoding for prosthetic control by using high-density EMG signals. This model offers more accurate estimation of joint positions compared to traditional electromyography-driven models.

Keywords:
Electromyography (EMG)Motor unitMusculoskeletal modelNeural drive

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Engineering

Background:

  • Electromyography-driven musculoskeletal models (E-DMMs) link user commands to joint positions but face limitations like signal crosstalk from surface EMG.
  • High-density (HD) EMG signal decomposition offers a solution by extracting neural drives for enhanced human-machine interfaces.

Purpose of the Study:

  • To propose and validate a novel neural-driven musculoskeletal model (N-DMM) for improved decoding of wrist and metacarpophalangeal (MCP) joint positions under constraints.
  • To compare the performance of the N-DMM against a conventional E-DMM in estimating joint movements.

Main Methods:

  • HD EMG signals were recorded from eight subjects during mirrored bilateral training with constrained and unconstrained limbs.
  • Fast independent component analysis (fICA) was used to extract motor unit discharges and estimate neural drives from EMG signals.
  • Neural drives served as input for the N-DMM to predict joint movements, with E-DMM used for comparison.

Main Results:

  • The N-DMM demonstrated superior performance in estimating joint positions compared to the E-DMM.
  • The proposed model showed potential for more accurate and robust decoding of continuous movements, even under joint constraints.

Conclusions:

  • The N-DMM offers a promising advancement for human-machine interfaces, particularly for prosthetic applications.
  • Further development of the N-DMM could significantly enhance control accuracy and robustness for amputees.