Development of soft actuators for stroke rehabilitation using deep learning

Abstract

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.