Summary
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Stroke-induced motor impairments include muscle weakness, spasticity, and abnormal intermuscular coordination. After treating these impairments, regaining motor function, especially for arm/hand function, is still often challenging, partly due to the complexity in the upper extremities (UE) motor function with many degrees of freedom at multi-joints. Hence, there is a critical need for rehabilitation strategies that aim to improve the repertoire of intermuscular coordination. Many one-fits-all therapeutic protocols have been proposed, but current therapies do not meet the need to improve intermuscular coordination and enhance motor function. Adaptive, personalized rehabilitation should be considered according to their specific conditions and severity of their disease. One effective approach is the use of a wearable muscle-network trainer to facilitate UE rehabilitation in stroke patients. However, the development and deployment of the muscle-network trainer pose significant challenges, particularly in determining the operating principle and
optimal parameters for assistance, as well as accurately identifying key stimulation points on the impaired arms. These challenges often result in the need for intensive human labor and reliance on potentially noisy or inaccurate test observations. To address these issues, developing and leveraging a digital twin (DT) of the muscle networks within the rehabilitation process emerges as a promising solution, which can optimize the configuration of the muscle-network trainer and enhance the understanding of underlying biological mechanisms. This project aims to explore the mathematical and engineering foundations of such DTs within the hybrid physical-digital realms. |
Team
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Ye Hu (Industrial and Systems Engineering), Leslie Morse (Physical Medicine and Rehabilitation), Yingchun Zhang (Biomedical Engineering)
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