Recently, healthcare and rehabilitation are becoming more and more important. Due to the advancement of technology, traditional medical equipment may produce great contributions after the combination of equipment and electronic technology. Nowadays, much medical equipment is not combined with electronic technology. Hence, doctors do not track patients’ courses of treatment. Moreover, when patients do rehabilitation exercises at home without the assistance of doctors or nurses, they cannot confirm that their rehabilitation motions are correct or not. It may affect the effectiveness of rehabilitation.
In this dissertation, we propose a new rehabilitation architecture. We connect traditional medical equipment with electronic technology. We assist doctors in understanding patients’ rehabilitation via the process of data collection, quantization, clustering, classification, and analysis. Also, by the process of clustering and classification, patients can determine whether their rehabilitation actions are correct or not and adjust their own rehabilitation actions accordingly to improve the effectiveness of rehabilitation.
In implementation, we apply our architecture to pelvic floor muscle training (PFMT). We cooperated with a doctor of the Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taiwan, and performed a series of experiments to verify our theory. The system has been implemented in medicine and has been used to treat patients with urinary incontinence in practice. We hope that this system can help more patients in the future.