Motion Detection and Evaluation of Chair Exercise Support System with Depth Image Sensor

Abstract

Sustaining lower limb functionality is extremely important in the preventative care of the elderly. Chair exercise, in which the exerciser sits on an ordinary chair, offers a way for seniors with little physical strength to exercise without a great deal of effort. Meanwhile, Microsoft's Kinect sensor that is capable of detecting human motion without the subject having to wear any kind of a special marker are becoming widely available. Exploiting this new sensor technology, this paper describes the development and deployment of a prototype lower limb chair exercise support system that takes full advantage of the Kinect depth sensor. The system supports five different chair exercises designed to strengthen the lower limbs, recognizes and evaluates exercises based on 3D position data and joint angles for each joint obtained from the Kinect sensor. The system illustrates how to do the exercises by voice instructions and model images, and superimposes the muscles used onto an image of the exerciser in real time. The system also provides exercise assessment results and advice by voice and text. In a series of trials involving seven elderly subjects in their late 70s and early 80s, an overall average recognition rate of 89% was obtained for the five exercises. Feedback was obtained through a questionnaire given to five male subjects ranging in age from 50 to 65, which highlighted a number of issues that should be addressed to improve the effectiveness of the system.