A Low-Cost Pressure Sensor Matrix for Activity Monitoring in Stroke Patients Using Artificial Intelligence

Abstract

Muscle weakening is a common consequence of stroke and can result in a reduction in physical activity of the affected body part. Therapy includes a range of physical exercises that will help the patients restore and build physical strength, endurance, flexibility, balance, and stability. In order to analyze and recognize the activity and movement of hands while performing these exercises, we have developed a $4\times4$ flexible pressure sensor matrix to quantize the performance and progress of a patient undergoing physiotherapy. We have also developed an artificial intelligence (AI) based algorithm to determine the accuracy of positioning by the patients. Experimental results demonstrate that the algorithm gives a mean error of 0.103 cm in detecting the position of load, compared to a mean error of 0.704 cm using mathematical analysis. With this system, a patient can be asked to move a weight to a particular location (as part of regular physiotherapy) and the pressure sensor matrix can be used to calculate error in positioning along with the time taken to complete the task. Advantages of the proposed pressure sensor matrix are cost effectiveness, facile fabrication, high sensitivity, robustness and flexibility.