Abstract
A cerebrovascular accident, often known as a stroke, is the world's second leading cause of mortality. It can cause paralysis, cognitive damage, and substantial dysfunction if it is not fatal. Post-stroke patients should engage in physiotherapy or rehabilitation to speed things up their healing and restore their mobility. The evaluation of a patient's rehabilitation needs is crucial in determining the best course of action. However, present evaluation techniques primarily rely on the therapist's experience, and assessment is performed sparingly due to therapist availability. Studies have discovered new approaches to support therapists in studying and assessing their patients, and also enabling therapy accessible to everyone, due to technological advancements. Robotic stroke rehabilitation by combining robot-assisted rehabilitation with gaming technologies has the possibility to supplement therapy and enhance stroke recovery, making recovery more efficient and productive. There is a need for a tool to guarantee that stroke patients do their recommended exercises correctly. The assistive robotics device can facilitate stroke patients to finish their workouts even without continuous surveillance of a therapist. The aim of this systematic review is to evaluate current advancements in the field of post-stroke rehabilitation integrating wearable technology for data collecting and machine learning techniques to evaluate the workouts.
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Selvaganapathy, S.G., Hema Priya, N., Rathika, P.D., Mohana Lakshmi, M. (2023). Machine Learning Based Robotic-Assisted Upper Limb Rehabilitation Therapies: A Review. In: Shukla, P.K., Singh, K.P., Tripathi, A.K., Engelbrecht, A. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-7892-0_6
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