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ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biomechanics
Volume 13 - 2025 | doi: 10.3389/fbioe.2025.1566381
This article is part of the Research Topic Biomechanics, Sensing and Bio-inspired Control in Rehabilitation and Assistive Robotics, Volume II View all 6 articles
Parameter Identification and Sensitivity Analysis of a Lower Limb Musculoskeletal Model
Provisionally accepted- 1 Harbin Engineering University, Harbin, China
- 2 School of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, Heilongjiang Province, China
- 3 Ningbo Second Hospital, Ningbo, Zhejiang Province, China
- 4 Tokyo Institute of Technology, Meguro City, Tōkyō, Japan
The estimation of joint torque based on wearable sensors is an important content in human-robot interaction research. Despite existing joint torque estimation models providing high accuracy, their application in robotic control is limited due to the number of sensors and real-time output requirements. To address this issue, the paper establishes a knee joint torque estimation model driven by four EMG sensors and proposes a novel method for simplifying musculoskeletal models based on sensitivity analysis. To achieve this, this paper combines multiple advanced Hill-type muscle model components to establish a knee joint musculoskeletal model that includes four major muscles, and employs the Genetic Algorithm (GA) to identify the model parameters. Then, Sobol's global sensitivity analysis theory is used to analyze the influence of parameter variations on model outputs, and proposes a sensitivity-based model simplification method. In addition, a lower limb physical and biological signal collection experiment without ground reaction force is designed for the parameter identification and sensitivity analysis. Finally, based on experimental data from several test subjects, the parameters of each individual's musculoskeletal model are identified and evaluated, and the sensitivity index of each parameter is calculated to determine the influence of the number of model parameters on the identification performance. The results show that the proposed musculoskeletal model can provide individuals with comparable normalized root mean square error (NRMSE) through parameter identification, and the sensitivity-based model simplification method is effective.
Keywords: Hill-type Muscle Model, Parameter identification, SEMG signal, sensitivity analysis, Joint torque estimation
Received: 24 Jan 2025; Accepted: 20 Mar 2025.
Copyright: © 2025 Li, Wang, Yuan and Deng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Keyi Wang, Harbin Engineering University, Harbin, China
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