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Updated: May 6, 2026

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Musculoskeletal modeling based on deep-nsNMF for multi-DoF motion decoding.

Jianmin Li1,2, Yue Yang1,2, Qiyang Li1,2

  • 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
|May 5, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Deep non-smooth Non-negative Matrix Factorization Musculoskeletal Model (Deep-nsNMF-MM) to improve decoding of multiple simultaneous movements using surface electromyography (sEMG) signals. The enhanced model significantly boosts accuracy for complex human-machine interfaces.

Keywords:
Decomposition algorithmHuman-machine interfaceMuscle synergyMusculoskeletal model

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Machine Learning

Background:

  • Surface electromyography (sEMG) signals are crucial for decoding muscle intention in human-machine interfaces (HMIs).
  • Existing musculoskeletal models using Non-negative Matrix Factorization (NMF) struggle with accuracy for multi-degree-of-freedom (DoF) movements due to muscle signal crosstalk.
  • The M-NMF-MM framework, while interpretable, shows limitations in precise decoding of simultaneous motions.

Purpose of the Study:

  • To develop an improved musculoskeletal model for enhanced decoding accuracy in HMIs.
  • To address the limitations of current NMF-based models in handling muscle signal crosstalk for multi-DoF movements.
  • To introduce a novel Deep non-smooth NMF (Deep-nsNMF) algorithm within a musculoskeletal model framework (Deep-nsNMF-MM).

Main Methods:

  • Integration of a Deep non-smooth NMF (Deep-nsNMF) algorithm to replace standard NMF in the M-NMF-MM.
  • The Deep-nsNMF algorithm enhances muscle co-excitation sparsity and reduces signal redundancy.
  • Evaluation of the Deep-nsNMF-MM against standard NMF and sparse NMF for decoding four DoF synchronous motions.

Main Results:

  • The Deep-nsNMF algorithm demonstrated higher variance accounted for (VAF) compared to other NMF variants, especially with increased network layers.
  • The Deep-nsNMF-MM significantly improved decoding accuracy for metacarpophalangeal and wrist movements compared to the original M-NMF-MM.
  • Pearson's correlation coefficients increased, and normalized root mean square error decreased by 10-15% for synchronous multi-DoF motions.

Conclusions:

  • The proposed Deep-nsNMF-MM offers a novel, high-precision decoding method for multi-DoF HMIs.
  • This physiologically interpretable model enhances synchronous proportional control by improving sEMG signal decoding.
  • The Deep-nsNMF approach effectively mitigates muscle signal crosstalk, advancing HMI capabilities.