Cloud-Powered Smart Rehabilitation and Assistive Robotics for Stroke Recovery with Recurrent Neural Networks

Innovative rehabilitation treatments are needed since stroke is a major cause of long-term impairment globally. In this paper, it presents a smart robotics-assisted rehabilitation system for stroke patients that uses cloud computing and is enhanced using Recurrent Neural Networks (RNNs). The system utilizes a combination of wearable sensors, robotic equipment, and cloud computing to create individualized rehabilitation plans for each patient. To quickly evaluate patients' progress and adjust rehabilitation programs, RNNs are used for real-time processing of continuous sensor data streams. Cloud computing allows for easy scaling, universal accessibility, and compatibility with preexisting healthcare systems. Initial results show promise in enhancing stroke recovery outcomes, highlighting the potential of the proposed method to transform rehabilitation techniques in the smart healthcare realm. The urgent need for effective and tailored stroke rehabilitation programs is something this framework intends to solve, which should lead to better results and a higher quality of life for patients.

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Publication Details

Published
2024-11-18
DOI
https://doi.org/10.1109/icdici62993.2024.10810898
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
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article

Cloud-Powered Smart Rehabilitation and Assistive Robotics for Stroke Recovery with Recurrent Neural Networks

M. Pallikonda Rajasekaran, J. Visumathi, Balachandra Pattanaik, M Sampoornam et al.
Stroke Rehabilitation and Recovery
article

Cloud-Powered Smart Rehabilitation and Assistive Robotics for Stroke Recovery with Recurrent Neural Networks

M. Pallikonda Rajasekaran, J. Visumathi, Balachandra Pattanaik, M Sampoornam, S. Sujatha, S. Kalaichelvi
article en

Abstract

Innovative rehabilitation treatments are needed since stroke is a major cause of long-term impairment globally. In this paper, it presents a smart robotics-assisted rehabilitation system for stroke patients that uses cloud computing and is enhanced using Recurrent Neural Networks (RNNs). The system utilizes a combination of wearable sensors, robotic equipment, and cloud computing to create individualized rehabilitation plans for each patient. To quickly evaluate patients' progress and adjust rehabilitation programs, RNNs are used for real-time processing of continuous sensor data streams. Cloud computing allows for easy scaling, universal accessibility, and compatibility with preexisting healthcare systems. Initial results show promise in enhancing stroke recovery outcomes, highlighting the potential of the proposed method to transform rehabilitation techniques in the smart healthcare realm. The urgent need for effective and tailored stroke rehabilitation programs is something this framework intends to solve, which should lead to better results and a higher quality of life for patients.

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), Wollega University (ET), Swami Vivekanand College of Pharmacy (IN), First Candle (US), Saveetha University (IN)
Openalex Percentile: Top 70%
Stroke Rehabilitation and Recovery
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