Monitoring frozen shoulder exercises to support clinical decision on treatment process using smartphone

• Main Authors: Nuntiya Chiensriwimol, Jonathan H. Chan, Pornchai Mongkolnam, Keerin Mekhora
• Other Authors: King Mongkut s University of Technology Thonburi
• Format: Conference or Workshop Item
• Published: 2018
• Subjects: Computer Science
• Online Access: https://repository.li.mahidol.ac.th/handle/123456789/42420
• Institution: Mahidol University

Continuously performing rehabilitation exercises are important for frozen shoulder patients to recover and increase their range of motion (ROM). Every time patients go to see their medical practitioner, they will be assigned home program rehabilitation exercises and the results of the ROM measurements will be measured on their return to their practitioner's clinic. At present, there are many kinds of technology that can detect movements. Many researchers have studied and used them to develop applications to facilitate tele-rehabilitation. This study aims to develop an application that supports a medical practitioners' decision making process. This application animates the exercise pattern of the patient and it is developed on Android and Web platforms. The web-based treatment process manages the patient's details, assigned tasks, and medical practitioners' profiles. The Android application part provides three exercise types: flexion, abduction, and horizontal flexion. These are useful for the patient in performing frozen shoulder tele-rehabilitation. We performed a preliminary study with five participants. The participants were assigned three exercise types according to the target angles set by the medical practitioner. When the subjects reached the target angle, an audio biofeedback was provided. In the experiment, the subjects needed to exercise in the time period set by the medical practitioners in order to track the shoulder rehabilitation progress. The preliminary results found that our application can possibly help medical practitioners in the treatment process and tele-rehabilitation tracking. In the future our application can be applied to other parts of the body and possibly modified to apply to stroke rehabilitation tracking.