A summative scoring system for evaluation of human kinematic performance

Pham, Trieu, Pathirana, Pubudu N., Won, Yonggwan and Li, Saiyi 2016, A summative scoring system for evaluation of human kinematic performance, Biomedical signal processing and control, vol. 23, pp. 85-92, doi: 10.1016/j.bspc.2015.08.003.

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Title A summative scoring system for evaluation of human kinematic performance
Author(s) Pham, Trieu
Pathirana, Pubudu N.ORCID iD for Pathirana, Pubudu N. orcid.org/0000-0001-8014-7798
Won, Yonggwan
Li, Saiyi
Journal name Biomedical signal processing and control
Volume number 23
Start page 85
End page 92
Total pages 8
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-01
ISSN 1746-8094
Keyword(s) Science & Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Medical Laboratory Technology
Human performance
Pattern classification
Motion matching
Elbow points
Summary Evaluation of human kinematic performance is essential in rehabilitation and skill assessment. These services are in high demand where the improvements made due to exercises need to be regularly assessed. In some relevant industries there is a need to evaluate their employee capabilities quantitatively for accident compensation and insurance purposes. In particular, these assessments are preferred to be based on more quantifiable measures in a standardized form ensuring accuracy, reliability, ease of use and anywhere anytime information to the clinician. Therefore, it is necessary to have an efficient mechanism for evaluation and assessment of human kinematic movements as the current motion matching and recognition algorithms fall short due to characteristically strict specifications required in numerous health care applications. In this paper, we propose a summative approach using a double integral to define a closeness between two trajectories typically generated by human movement. This approach can be considered as a spatial scoring mechanism in the evaluation of human kinematic performance as well as in movement recognition applications. Several experiments based on computer simulations as well as real data were set up to examine the performance of the proposed approach as a scoring mechanism for the evaluation of human kinematic performances. The results demonstrated better characterization of the movement assessment and motion recognition ability, with a recognition rate of 86.19%, than the currently used methods such as Gaussian mixture models and pose normalization employed in motion recognition tasks. Finally, we use the scoring mechanism to analyze the proximity in human kinematic performance.
Language eng
DOI 10.1016/j.bspc.2015.08.003
Field of Research 0903 Biomedical Engineering
1004 Medical Biotechnology
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30078492

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