Enhancing Hybrid Exoskeleton Performance With Spatially Distributed Asynchronous Stimulation and Ultrasound Imaging-Based Fatigue Measurements

This paper introduces an innovative approach, Spatially distributed asynchronous stimulation (SDAS), designed to enhance the fatigue resistance of electrically stimulated muscles for mobility assistance in individuals with spinal cord injury (SCI), stroke, and multiple sclerosis. The study focuses o...

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Bibliographic Details
Main Authors: Ashwin Iyer, Vidisha Ganesh, Mayank Singh, William Filer, Christine Cleveland, Nitin Sharma
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
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Online Access:https://ieeexplore.ieee.org/document/11030739/
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Summary:This paper introduces an innovative approach, Spatially distributed asynchronous stimulation (SDAS), designed to enhance the fatigue resistance of electrically stimulated muscles for mobility assistance in individuals with spinal cord injury (SCI), stroke, and multiple sclerosis. The study focuses on modulating stimulation intensity in multiple distributed electrodes through a phenomenological model of SDAS to increase muscle power output. A model predictive control (MPC) approach is used to design optimal SDAS intensity profiles for knee extension tracking. The study also establishes a strong correlation between Ultrasound (US)-derived strain measurements of the quadriceps muscle and the model’s fatigue parameter. The model is extended to predict non-isometric knee extension and integrated with US-derived feedback from the quadriceps muscle to control stepping with the hybrid exoskeleton. The results demonstrate that the optimized SDAS inputs and US-derived fatigue measurements of spatially separated muscle regions during SDAS can effectively delay fatigue and increase muscle power output. This study underscores the importance of a model-based closed-loop SDAS approach and the integration of US-derived feedback in the design of a novel fatigue-resistant SDAS-based gait assistance technology.
ISSN:1534-4320
1558-0210