Human Action Understanding and Movement Error Identification for the Treatment of Patients with Parkinson's Disease
Wenchuan Wei, Carter McElroy, Sujit Kumar Dey · 2018
Traditional physical therapy treatment for patients with Parkinson's disease (PD) requires regular visits with the physical therapist (PT), which may be expensive and inconvenient. In this paper, we propose a learning-based personalized treatment system to enable home-based training for PD patients. It uses the Kinect sensor to monitor the patient's movements at home. Three physical therapy tasks with multiple difficulty levels are selected by our PT co-author to help PD patients improve balance and mobility. Criteria for each task are carefully designed such that patient's performance can be automatically evaluated by the proposed system. Given the patient's motion data, we propose a two-phase human action understanding algorithm TPHAU to understand the patient's movements. To evaluate patient performance, we use Support Vector Machine to identify the patient's error in performing the task. Therefore, the patient's error can be reported to the PT, who can remotely supervise the patient's performance and conformance on the training tasks. Moreover, the PT can update the tasks that the patient should perform through the cloud-based platform in a timely manner, which enables personalized treatment for the patient. To validate the proposed approach, we have collected data from PD patients in the clinic. Experiments on real patient data show that the proposed methods can accurately understand patient's actions and identify patient's movement error in performing the task.