Version 1
: Received: 4 November 2024 / Approved: 4 November 2024 / Online: 5 November 2024 (09:31:01 CET)
How to cite:
Hasan, S.; Alam, N.; Mashud, G. A.; Bhujel, S. Neural Network for Enhancing Robot Assisted Rehabilitation: A Systematic Review. Preprints2024, 2024110183. https://doi.org/10.20944/preprints202411.0183.v1
Hasan, S.; Alam, N.; Mashud, G. A.; Bhujel, S. Neural Network for Enhancing Robot Assisted Rehabilitation: A Systematic Review. Preprints 2024, 2024110183. https://doi.org/10.20944/preprints202411.0183.v1
Hasan, S.; Alam, N.; Mashud, G. A.; Bhujel, S. Neural Network for Enhancing Robot Assisted Rehabilitation: A Systematic Review. Preprints2024, 2024110183. https://doi.org/10.20944/preprints202411.0183.v1
APA Style
Hasan, S., Alam, N., Mashud, G. A., & Bhujel, S. (2024). Neural Network for Enhancing Robot Assisted Rehabilitation: A Systematic Review. Preprints. https://doi.org/10.20944/preprints202411.0183.v1
Chicago/Turabian Style
Hasan, S., Gazi Abdullah Mashud and Subodh Bhujel. 2024 "Neural Network for Enhancing Robot Assisted Rehabilitation: A Systematic Review" Preprints. https://doi.org/10.20944/preprints202411.0183.v1
Abstract
Recently, the integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability to interpret complex physiological signals. Surface electromyography (sEMG), electromyography (EMG), electroencephalography (EEG), and other physiological signals enable communication between the human body and robotic systems. Utilizing physiological signals for communicating with robots plays a crucial role in robot assisted neurorehabilitation. This systematic review synthesizes 44 peer-reviewed studies, exploring how neural networks can improve exoskeleton robot assisted rehabilitation for individuals with impaired upper limbs. By categorizing the studies based on robot assisted joints, sensor systems, and control methodologies, we offer a comprehensive overview of neural network applications in this field. Our findings demonstrate that neural networks, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Radial Basis Function Neural Networks (RBFNN), and other forms of neural network significantly contribute to patient specific rehabilitation by enabling adaptive learning and personalized therapy. CNNs improve motion intention estimation and control accuracy, while LSTM networks capture temporal muscle activity patterns for real-time rehabilitation. RBFNNs improve human-robot interaction by adapting to individual movement patterns, leading to more personalized and efficient therapy. This review highlights the potential of neural networks to revolutionize upper limb rehabilitation, improving motor recovery and patient outcomes in both clinical and home-based settings. It also recommends the future direction to customize existing neural networks for robot assisted rehabilitation applications.
Keywords
CNN; RNN; LSTM; sEMG; RBFNN; Fuzzy and Deep Neural Network; Upper Limb Rehabilitation
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
