Recovery Prediction in the Framework of Cloud-Based Rehabilitation Exergame

Abstract

In this paper, we propose a framework of a cost-effective, entertaining, and motivating home-based upper limb rehabilitation system which consists of a cloud system and a client interface. The framework provides real-time feedback to the patient subject, summarizes the feed-back after each session, and predicts the rehabilitation performance. As an implementation of the framework, a Kinect sensor is used to collect real-time data for upper limb joints of the subjects while they are participating in rehabilitation exergames. The Dynamic Time Warping (DTW) algorithm is then applied to compare the movement pattern of a patient subject with the movement pattern of a healthy subject. Next, the Auto-Regressive Integrated Moving Average (ARIMA) is utilized to forecast the rehabilitation progress of the patients based on their performance history. The prototype of this system is tested on six healthy individuals and one patient. The results show that the patients’ movement patterns have a similar curve shape to the healthy individuals’ movement patterns and, hence, the DTW algorithm can be used as an effective index to describe the rehabilitation statuses of the subjects. The forecasting method is briefly tested by feeding the rehabilitation status history.