DocumentCode :
1526285
Title :
An Adaptive Mixed Reality Training System for Stroke Rehabilitation
Author :
Duff, Margaret ; Chen, Yinpeng ; Attygalle, Suneth ; Herman, Janice ; Sundaram, Hari ; Qian, Gang ; He, Jiping ; Rikakis, Thanassis
Author_Institution :
Sch. of Biol. & Health Syst. Eng., Arizona State Univ., Tempe, AZ, USA
Volume :
18
Issue :
5
fYear :
2010
Firstpage :
531
Lastpage :
541
Abstract :
This paper presents a novel mixed reality rehabilitation system used to help improve the reaching movements of people who have hemiparesis from stroke. The system provides real-time, multimodal, customizable, and adaptive feedback generated from the movement patterns of the subject´s affected arm and torso during reaching to grasp. The feedback is provided via innovative visual and musical forms that present a stimulating, enriched environment in which to train the subjects and promote multimodal sensory-motor integration. A pilot study was conducted to test the system function, adaptation protocol and its feasibility for stroke rehabilitation. Three chronic stroke survivors underwent training using our system for six 75-min sessions over two weeks. After this relatively short time, all three subjects showed significant improvements in the movement parameters that were targeted during training. Improvements included faster and smoother reaches, increased joint coordination and reduced compensatory use of the torso and shoulder. The system was accepted by the subjects and shows promise as a useful tool for physical and occupational therapists to enhance stroke rehabilitation.
Keywords :
biomechanics; patient rehabilitation; virtual reality; adaptive feedback; adaptive mixed reality training system; arm; hemiparesis; joint coordination; multimodal sensory-motor integration; reaching movement; stroke rehabilitation; torso; Art; Biological materials; Biomedical engineering; Feedback; Kinematics; Medical treatment; Permission; Systems engineering and theory; Virtual reality; Mixed reality; motion analysis; reach and grasp; stroke rehabilitation; upper extremity; Aged; Artificial Intelligence; Biofeedback, Psychology; Female; Humans; Male; Software; Stroke; Therapy, Computer-Assisted; Treatment Outcome; User-Computer Interface;
fLanguage :
English
Journal_Title :
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1534-4320
Type :
jour
DOI :
10.1109/TNSRE.2010.2055061
Filename :
5497185
Link To Document :
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