DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sheng, Bo | ko |
dc.contributor.author | Wang, Xiangbin | ko |
dc.contributor.author | Xiong, Shuping | ko |
dc.contributor.author | Hou, Meijin | ko |
dc.contributor.author | Zhang, Yanxin | ko |
dc.date.accessioned | 2020-01-19T06:20:15Z | - |
dc.date.available | 2020-01-19T06:20:15Z | - |
dc.date.created | 2020-01-15 | - |
dc.date.created | 2020-01-15 | - |
dc.date.issued | 2019-11-22 | - |
dc.identifier.citation | 4th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2019, pp.45 - 51 | - |
dc.identifier.uri | http://hdl.handle.net/10203/271482 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Kinematic metrics for upper-limb functional assessment of stroke patients | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85080140229 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 45 | - |
dc.citation.endingpage | 51 | - |
dc.citation.publicationname | 4th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2019 | - |
dc.identifier.conferencecountry | CC | - |
dc.identifier.conferencelocation | Shanghai Institute of Technology | - |
dc.identifier.doi | 10.1109/ICIIBMS46890.2019.8991507 | - |
dc.contributor.localauthor | Xiong, Shuping | - |
dc.contributor.nonIdAuthor | Sheng, Bo | - |
dc.contributor.nonIdAuthor | Wang, Xiangbin | - |
dc.contributor.nonIdAuthor | Hou, Meijin | - |
dc.contributor.nonIdAuthor | Zhang, Yanxin | - |
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