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Shoulder joint rehabilitation exercises are considered one of the most effective treatments for reducing shoulder pain and improving the range of motion. In addition to regular supervision from professional rehabilitation staff, participation in home-based self-practice can enhance the effectiveness of treatment. Therefore, this study proposes an intelligent monitoring system for home-based self-rehabilitation. In this system, smart phones serve as the platform for integrating an accelerometer-based sensor network for monitoring the performance of rehabilitation exercises by patients with shoulder injuries. The developed sensor network comprises 2 accelerometer sensors and the built-in smart phone accelerometer that communicate using Bluetooth protocols. The following 5 monitoring exercises were included in this study: touch ear, use fingers to climb wall both facing and sideways to the wall, clockwise and counterclockwise pendulum circles, active-assisted front and side stretches, and raises hand from back. Shoulder rehabilitation activities are recognized by the Support Vector Machine algorithms and recorded on the smart phone. These records can be used by patients as a reference of their activity. The records can also be uploaded to the hospital server to assist physicians in monitoring the effectiveness of exercises. The proposed approach is low cost and can be extended to various monitoring targets by simply installing a new Android app.
Pan et al. (Mon,) studied this question.