College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, 01003, USA.
Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH, 43210, USA.
J Med Syst. 2018 Nov 7;42(12):255. doi: 10.1007/s10916-018-1113-4.
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.
虚拟现实康复在改善上肢功能和日常生活活动能力方面的效果至少与传统护理一样好。由于低成本游戏系统的出现以及患者对基于游戏的治疗的偏好，视频游戏技术在未来几年可能会越来越多地应用于物理治疗实践。结合低成本运动捕捉技术的游戏系统通常会生成大量在康复过程中进行的治疗运动的数据集。然而，尚未建立基础设施来实现对在不受控制的实验室环境之外收集的大量运动数据的有效处理。在本文中，提出了一种从不受控制和未监控环境中进行的基于游戏的康复中提取和评估治疗运动的方法。通过克服这些挑战，个体康复轨迹的有意义运动学分析成为可能。此外，这种方法为分析在不受控制的临床环境中生成的大型数据集提供了一种手段，从而能够更好地预测康复潜力和个性化医疗的剂量反应关系。
