Computer Vision-Based Classification of Hand Grip Variations in Neurorehabilitation

被引:0
作者
Zariffa, Jose [1 ]
Steeves, John D. [1 ]
机构
[1] Univ British Columbia, ICORD, Vancouver, BC V5Z 1M9, Canada
来源
2011 IEEE INTERNATIONAL CONFERENCE ON REHABILITATION ROBOTICS (ICORR) | 2011年
关键词
robotic rehabilitation; hand posture; computer vision; cylindrical grasp; lateral key grip; tip-to-tip pinch; RECOVERY; PATIENT; TRIAL;
D O I
暂无
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
The complexity of hand function is such that most existing upper limb rehabilitation robotic devices use only simplified hand interfaces. This is in contrast to the importance of the hand in regaining function after neurological injury. Computer vision technology has been used to identify hand posture in the field of Human Computer Interaction, but this approach has not been translated to the rehabilitation context. We describe a computer vision-based classifier that can be used to discriminate rehabilitation-relevant hand postures, and could be integrated into a virtual reality-based upper limb rehabilitation system. The proposed system was tested on a set of video recordings from able-bodied individuals performing cylindrical grasps, lateral key grips, and tip-to-tip pinches. The overall classification success rate was 91.2%, and was above 98% for 6 out of the 10 subjects.
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页数:4
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