Home-Based Online Multisensory Arm Rehabilitation Monitoring System

Abstract

Arm rehabilitation activities need to be monitored continuously in terms of analysis by experts within sufficient information to discover arm dysfunction and disorders such as stroke early. Although there are numerous previous researches about the home-based rehabilitation procedures and arm performance, some drawbacks still exist. For example, current rehabilitation devices are too complicated and required supervision by qualified therapists rather than their high prices. Moreover, data from these devices take much time to be sent to doctors for monitoring purpose. Therefore, this paper proposes a home-based online multisensory arm rehabilitation monitoring system. This system is designed through three main stages which are data acquisition, data processing, and data logging respectively. Data acquisition is by three different types of sensors; flex sensor (for arm's bending), force-sensitive resistors (for hand fingers' forces) and accelerometer (for arm movement directions). In data processing, Arduino Mega Controller and ESP Wi-Fi shield are used to design Internet of Things (IoT) web-based system (ThingSpeak) for data logging that allows doctors to diagnose stroke recovery and give feedbacks for patients. Overall, this project showed a robust system for arm rehabilitation using portable, user-friendly, and low power consumption devices at a low cost.