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    Quantitative Evaluation of the Microsoft Kinect for Use in an Upper Extremity Virtual Rehabilitation Environment

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    C23_Nixon_ICVR2013.pdf (235.9Kb)
    Date
    2013
    Author
    Nixon, Mason
    Chen, Yu-ping
    Howard, Ayanna M.
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    Abstract
    Low cost depth sensors could potentially allow for home-based care and rehabilitation using virtual systems. Currently, no publicly available and peer-reviewed assessment has been made on the accuracy of joint position data determined by the Microsoft KinectTM for assessment of upper extremity movements. We devised and validated clinically-based angle classifications for random arm movements in 3D-space, specifically, the shoulder joint flexion/extension angle, shoulder joint abduction/adduction angle, and 3-dimensional shoulder joint angle of 19 subjects at a distance of 2.0m using an eight camera Vicon Motion Capture system. Results show an average absolute error of these angle measurements not exceeding 10.0%.
    URI
    http://hdl.handle.net/1853/48212
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    • Human-Automation Systems Lab (HumAns) [153]
    • Human-Automation Systems Lab HumAns Publications [153]

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