4.6 Article

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

Journal

JOURNAL OF MEDICAL SYSTEMS
Volume 42, Issue 12, Pages -

Publisher

SPRINGER
DOI: 10.1007/s10916-018-1113-4

Keywords

Stroke; Telerehabilitation; Serious games; Kinect; Motion capture; Motor rehabilitation; Hemiparesis; Signal processing; Clustering algorithm; Probability density function

Funding

  1. National Multiple Sclerosis Society
  2. Rudi Schulte Foundation, Santa Barbara, CA
  3. National Center for Advancing Translational Sciences [8UL1TR000090-05]
  4. Participant-Centered Outcomes Research Institute (PCORI)
  5. Rudi Schulte Foundation
  6. NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES [UL1TR002733] Funding Source: NIH RePORTER

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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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