A Novel Methodology for Extracting and Evaluating Therapeutic Movements in Game-Based Motion Capture Rehabilitation Systems

被引:50
|
作者
Yang, Zhichao [1 ]
Rafiei, Mohammad H. [2 ,3 ,4 ,5 ,6 ]
Hall, Alexis [2 ]
Thomas, Caroline [2 ]
Midtlien, Hali A. [2 ]
Hasselbach, Alexander [2 ]
Adeli, Hojjat [3 ,4 ,5 ]
Gauthier, Lynne V. [2 ,6 ]
机构
[1] Univ Massachusetts, Coll Informat & Comp Sci, Amherst, MA 01003 USA
[2] Ohio State Univ, Dept Phys Med & Rehabil, Columbus, OH 43210 USA
[3] Ohio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA
[4] Ohio State Univ, Dept Neurol, Columbus, OH 43210 USA
[5] Ohio State Univ, Dept Neurosci, Columbus, OH 43210 USA
[6] Univ Massachusetts, Dept Phys Therapy, Lowell, MA 01854 USA
关键词
Stroke; Telerehabilitation; Serious games; Kinect; Motion capture; Motor rehabilitation; Hemiparesis; Signal processing; Clustering algorithm; Probability density function; MOTOR FUNCTION-TEST; VIRTUAL-REALITY; STROKE; MODEL; ARM;
D O I
10.1007/s10916-018-1113-4
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Virtual rehabilitation yields outcomes that are at least as good as traditional care for improving upper limb function and the capacity to carry out activities of daily living. Due to the advent of low-cost gaming systems and patient preference for game-based therapies, video game technology will likely be increasingly utilized in physical therapy practice in the coming years. Gaming systems that incorporate low-cost motion capture technology often generate large datasets of therapeutic movements performed over the course of rehabilitation. An infrastructure has yet to be established, however, to enable efficient processing of large quantities of movement data that are collected outside of a controlled laboratory setting. In this paper, a methodology is presented for extracting and evaluating therapeutic movements from game-based rehabilitation that occurs in uncontrolled and unmonitored settings. By overcoming these challenges, meaningful kinematic analysis of rehabilitation trajectory within an individual becomes feasible. Moreover, this methodological approach provides a vehicle for analyzing large datasets generated in uncontrolled clinical settings to enable better predictions of rehabilitation potential and dose-response relationships for personalized medicine.
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页数:14
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