Duff Margaret, Chen Yinpeng, Attygalle Suneth, Sundaram Hari, Rikakis Thanassis
School of Biological and Health Systems Engineering and the School of Arts, Media & Engineering at Arizona State University, Tempe, AZ 85287, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5899-902. doi: 10.1109/IEMBS.2010.5627537.
This paper presents results from a clinical study of stroke survivors using an adaptive, mixed-reality rehabilitation (AMRR) system for reach and grasp therapy. The AMRR therapy provides audio and visual feedback on the therapy task, based on detailed motion capture, that places the movement in an abstract, artistic context. This type of environment promotes the generalizability of movement strategies, which is shown through kinematic improvements on an untrained reaching task and higher clinical scale scores, in addition to kinematic improvements in the trained task.
本文介绍了一项针对中风幸存者的临床研究结果，该研究使用了一种用于伸展和抓握治疗的自适应混合现实康复（AMRR）系统。AMRR疗法基于详细的动作捕捉，为治疗任务提供音频和视觉反馈，将动作置于抽象的艺术情境中。这种环境促进了运动策略的通用性，这不仅体现在经过训练的任务中运动学方面的改善，还体现在未经训练的伸展任务的运动学改善以及更高的临床量表评分上。
