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Machine Learning Based Robotic-Assisted Upper Limb Rehabilitation Therapies: A Review

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Computer Vision and Robotics

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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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Author information

Authors and Affiliations

  1. Department of Information Technology, PSG College of Technology, Coimbatore, India

    Shymala Gowri Selvaganapathy, N. Hema Priya & M. Mohana Lakshmi

  2. Department of Robotics & Automation Engineering, PSG College of Technology, Coimbatore, India

    P. D. Rathika

Authors
  1. Shymala Gowri Selvaganapathy
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  2. N. Hema Priya
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  3. P. D. Rathika
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  4. M. Mohana Lakshmi
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Corresponding author

Correspondence to Shymala Gowri Selvaganapathy .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Babu Banarasi Das University, Lucknow, Uttar Pradesh, India

    Praveen Kumar Shukla

  2. Department of Information Technology, Indian Institute of Information Technology, Allahabad, Prayagraj, Uttar Pradesh, India

    Krishna Pratap Singh

  3. Department of Computer Science & Engineering, Malviya National Institute of Technology (MNIT), Jaipur, India

    Ashish Kumar Tripathi

  4. Department of Industrial Engineering and Computer Science Division, University of Stellenbosch, Matieland, South Africa

    Andries Engelbrecht

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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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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