Comparison of the Performance of Single-Joint and Biarticular Muscle Synergies with Outputs from AnyBody and OpenSim During Walking

Articles in Press

Document Type : Original research papers

Authors

Department of Sport Biomechanics and Injury, Faculty of Physical Education and Sports Sciences, Kharazmi University, Tehran, Iran

10.22098/jast.2025.16377.1395

Abstract

Introduction and Objective: Considering the importance of assessing synergies and patterns of single-joint and biarticular muscles during walking for improving the diagnosis and management of orthopedic and neurological conditions, this study aimed to compare the performance of single-joint and biarticular muscle synergies with outputs from AnyBody and OpenSim during walking.

Methodology: In this study, kinetic, kinematic, and electromyography (EMG) data of the lower limb were collected from 32 participants during self-selected speed walking. Kinetic and kinematic data processed through AnyBody and OpenSim software were compared with normalized EMG data using the SPM method. Additionally, the temporal EMG correlation matrix was utilized to analyze synergist muscles.

Results: SPM analysis indicated good agreement between normalized EMG data and the outputs from AnyBody and OpenSim software. However, during certain phases of the gait cycle, normalized EMG values reached higher peaks (p < 0.05). The temporal EMG correlation matrix during walking revealed the following:

• Quadriceps muscles showed strong correlations (above 0.92) during knee extension.

• Hamstrings exhibited moderate to strong correlations (0.67) during knee flexion and hip extension.

• Calf muscles demonstrated strong correlations (above 0.94) during ankle plantarflexion.

• Gluteus medius and tensor fasciae latae showed strong correlations (0.9) during hip abduction and pelvic stabilization.

Conclusion: The results indicated that AnyBody was more accurate in estimating single-joint muscle activity, while OpenSim performed better in analyzing biarticular muscles and more complex movement patterns. These findings could contribute to improving simulation models and developing new strategies for diagnosing and treating movement disorders.

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Journal of Advanced Sport Technology

Articles in Press, Accepted Manuscript
Available Online from 11 August 2026

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History

  • Receive Date: 15 December 2024
  • Revise Date: 02 February 2025
  • Accept Date: 10 February 2025

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