A Depth Camera Motion Analysis Framework for Tele-rehabilitation: Motion Capture and Person-Centric Kinematics Analysis

• Published in: IEEE journal of selected topics in signal processing Vol. 10; no. 5; pp. 877 - 887
• Main Authors: Minxiang Ye, Cheng Yang, Stankovic, Vladimir, Stankovic, Lina, Kerr, Andrew
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithm design and analysis; Algorithms; Cameras; Depth image processing; feature extraction; Gait; Kinematics; motion analysis; Motion perception; Optical imaging; Optical sensors; Portability; Signal processing; signal processing for rehabilitation; tele-rehabilitation; Three-dimensional displays
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2016.2559446

With increasing importance given to tele-rehabilitation, there is a growing need for accurate, low-cost, and portable motion capture systems that do not require specialist assessment venues. This paper proposes a novel framework for motion capture using only a single depth camera, which is portable and cost effective compared to most industry-standard optical systems, without compromising on accuracy. Novel signal processing and computer vision algorithms are proposed to determine motion patterns of interest from infrared and depth data. In order to demonstrate the proposed framework's suitability for rehabilitation, we developed a gait analysis application that depends on the underlying motion capture sub-system. Each subject's individual kinematics parameters, which are unique to that subject, are calculated and stored for monitoring individual progress of the clinical therapy. Experiments were conducted on 14 different subjects, 5 healthy, and 9 stroke survivors. The results show very close agreement of the resulting relevant joint angles with a benchmarking 12-camera based VICON system, a mean error of at most 1.75 % in detecting gait events w.r.t. the hand-labeled ground-truth, and significant performance improvements in terms of accuracy and efficiency compared to a previous Kinect-based system.