Reconstructive Approach for Detection and Analysis of Hemiplegia in Human Walking Motion Based on Musculoskeletal Model

This study aims to explore the potential of a reconstructive approach in detecting and analyzing hemiplegia in human walking motion using a musculoskeletal model. Hemiplegia, a neurological disorder characterized by partial paralysis of one side of the body, is a common complication of stroke that can significantly impact its victim's mobility and overall quality of life [10]. We employed a combination of surface electromyography (sEMG) and motion capture data collection techniques to study healthy individuals' muscle activation and kinematics both in normal conditions and while wearing a hemiplegia simulator. The collected data were analyzed using a non-negative matrix factorization (NMF) algorithm to decompose the muscle acti-vation into distinct muscle synergies and associated weights. We then compared the number of muscle synergies used between the healthy and simulated hemiplegia conditions to identify changes in muscle coordination patterns. Using the information obtained from the comparison, an attempt was made to construct the synergies of the hemiplegia gait from the synergies of the healthy gait by adjusting the weights. The results of this study suggest that a reconstructive approach is a feasible tool for detecting and analyzing hemi-plegia in a human walking motion. Furthermore, this study provides a framework for understanding the underlying muscle coordination patterns in hemiplegia and can serve as a basis for developing new rehabilitation strategies. This study is expected to contribute to the advancement of research in this field by providing insights into the neural control of human walking motion.