Volume 8, Issue 11 (November 2021), Pages: 22-29
Original Research Paper
Title: Development of soft actuators for stroke rehabilitation using deep learning
Author(s): Naif Khalaf Al-Shammari 1, *, Ahmed S. Alshammari 2, Saleh Mohammd Albadarn 2, Syed Thouheed Ahmed 3, Syed Muzamil Basha 3, Ahmed A. Alzamil 4, Ahmed Maher Gabr 4
1Mechanical Engineering, University of Ha’il, Ha’il, Saudi Arabia
2Electrical Engineering Department, University of Ha’il, Ha’il, Saudi Arabia
3School of Computing and Information Technology, REVA University, Bangalore, India
4Physical Therapy Department, Faculty of Applied Medical Sciences, University of Ha’il, Ha’il, Saudi Arabia
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* Corresponding Author.
Corresponding author's ORCID profile: https://orcid.org/0000-0001-5100-267X
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Automation has created a mind-blowing impact in diversified fields all over the world. Not only in business but also in various domains like health care sectors, manufacturing, etc. a faultless execution is a prime concern. Robotic Process Automation has paved the way for research in the mechanical and mechatronics field. Software robots are trained well to complete repetitive tasks in an efficient manner. A design of such a soft robot can be greatly helpful in the arena of healing. Automation of Rehabilitation therapy has gained attention in recent years. The main aspiration towards the conduct of this research work is to accomplish a soft exoskeleton robot using a thin McKibben actuator applying Deep Learning approaches to aid automatic therapy to the paralyzed patients and help them carry out the hand movement-based exercises. Convolutional Neural Network (CNN) algorithm will be used to support the training of the AI-enabled automated device. The proposed methodology will support stroke survivors to perform exercises independently to enhance their hand motor recovery. For this purpose, it involves pneumatic soft actuator technology using thin McKibben artificial muscles to create a cognitive potential to induce rehabilitation. A soft actuator is proposed so as to confirm the safety purposes of stroke patients.
© 2021 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Soft exoskeleton robot, McKibben actuator, Convolutional neural network, Pneumatic soft actuator
Article History: Received 23 May 2021, Received in revised form 10 August 2021, Accepted 26 August 2021
This research has been funded by Scientific Research Deanship at the University of Ha’il-Saudi Arabia through project number RG-191344.
Compliance with ethical standards
Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Al-Shammari NK, Alshammari AS, and Albadarn SM et al. (2021). Development of soft actuators for stroke rehabilitation using deep learning. International Journal of Advanced and Applied Sciences, 8(11): 22-29
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