A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems

Karan Taneja, Xiaolong He, Qi Zhi He, Xinlun Zhao, Yun An Lin, Kenneth J. Loh, Jiun Shyan Chen

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Identification of muscle-tendon force generation properties and muscle activities from physiological measurements, e.g., motion data and raw surface electromyography (sEMG), offers opportunities to construct a subject-specific musculoskeletal (MSK) digital twin system for health condition assessment and motion prediction. While machine learning approaches with capabilities in extracting complex features and patterns from a large amount of data have been applied to motion prediction given sEMG signals, the learned data-driven mapping is black-box and may not satisfy the underlying physics and has reduced generality. In this work, we propose a feature-encoded physics-informed parameter identification neural network (FEPI-PINN) for simultaneous prediction of motion and parameter identification of human MSK systems. In this approach, features of high-dimensional noisy sEMG signals are projected onto a low-dimensional noise-filtered embedding space for the enhancement of forwarding dynamics prediction. This FEPI-PINN model can be trained to relate sEMG signals to joint motion and simultaneously identify key MSK parameters. The numerical examples demonstrate that the proposed framework can effectively identify subject-specific muscle parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion predictions of elbow flexion-extension motion that are in good agreement with the measured joint motion data.

Original languageEnglish (US)
JournalJournal of biomechanical engineering
Issue number12
StatePublished - Dec 1 2022

Bibliographical note

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Copyright © 2022 by ASME; reuse license CC-BY 4.0.


  • data-driven computing
  • feature-encoding
  • musculoskeletal system
  • parameter identification
  • physics-informed neural networks
  • surface electromyography


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