Identifying the Arm Joint Dynamics Using Muscle Synergy Patterns and SVMD-BiGRU Hybrid Mechanism

Seyyed Ali  Zendehbad; Hamid Reza  Kobravi; Mohammad Mahdi  Khalilzadeh; Athena Sharifi  Razavi; Payam Sasan  Nezhad

doi:10.18502/fbt.v13i2.21946

Seyyed Ali Zendehbad Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Hamid Reza Kobravi Research Center of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Mohammad Mahdi Khalilzadeh Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Athena Sharifi Razavi Clinical Research Development Unit of Bou Ali Sina Hospital, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
Payam Sasan Nezhad Ghaem Medical Center, Department of Neurology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

DOI: https://doi.org/10.18502/fbt.v13i2.21946

Keywords: Arm; Electromyography; Joint Dynamics; Muscle Synergy.

Abstract

Purpose: In this study, we propose a novel generalizable hybrid underlying mechanism for mapping Human Pose Estimation (HPE) data to muscle synergy patterns, which can be highly efficient in improving visual biofeedback.

Materials and Methods: In the first step, Electromyography (EMG) data from the upper limb muscles of twelve healthy participants are collected and pre-processed, and muscle synergy patterns are extracted from it. Concurrently, kinematic data are detected using the OpenPose model. Through synchronization and normalization, the Successive Variational Mode Decomposition (SVMD) algorithm decomposes synergy control patterns into smaller components. To establish mappings, a custom Bidirectional Gated Recurrent Unit (BiGRU) model is employed. Comparative analysis against popular models validates the efficacy of our approach, revealing the generated trajectory as potentially ideal for visual biofeedback. Remarkably, the combined SVMD-BiGRU model outperforms the alternatives.

Results: The results show that the trajectory generated by the model is potentially suitable for visual biofeedback systems. Remarkably, the combined SVMD-BiGRU model outperforms the alternatives. Furthermore, empirical assessments have demonstrated the adept ability of healthy participants to closely adhere to the trajectory generated by the model output during the test phase.

Conclusion: Ultimately, incorporating this innovative mechanism at the heart of visual biofeedback systems has been revealed to significantly elevate both the quantity and quality of movement.