Kinematic metrics for upper-limb functional assessment of stroke patients

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Upper-limb functional assessment is important for stroke treatment. The identification of sensitive kinematic metrics that best differentiate the impairment level of upper-limb motor function can enhance this assessment. Therefore, this research proposed a method to select sensitive kinematic metrics which can discriminate between stroke patients and healthy subjects. A total of 26 participants (10 healthy subjects and 16 stroke patients) were recruited to perform upper-limb reaching movements. The movement data was measured using Kinect v2. Thirty-two metrics were then extracted. Independent samples T-test, Mann-Whitney U-test and principal component analysis were performed to select sensitive metrics. Experimental results show that the first principal component explained 54.67% of the total variance, and it can distinguish stroke patients from healthy subjects. Meanwhile, loading values of index of curvature and spectral arc-length were 0.895 and 0.831 respectively, which contributed most for the first principal component. Therefore, we concluded that the sensitive metrics were index of curvature and spectral arc-length, which had significant importance to differentiate stroke patients from healthy subjects.
Institute of Electrical and Electronics Engineers Inc.
Issue Date

4th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2019, pp.45 - 51

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IE-Conference Papers(학술회의논문)
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