Abstract
Marker-less motion analysis system is the future for sports motion study. This is because it can potentially be applied in real time competitive matches because no marking system is required. The purpose of this study is to observe the suitability and practicality of one of the basic marker-less motion analysis system applications on human movement from sagittal view plane. In this study, the movement of upper and lower extremities of the human body during a vertical jump act was chosen as the movement to be observed. One skilled volleyball player was recruited to perform multiple trials of the vertical jump (n=90). All trials were recorded by one depth camera and one Full HD video camera. The kinematics of shoulder joint was chosen to represent the upper body extremity movement while knee joint was chosen as the representative of the lower body extremity movement during the vertical jump’s initial position to take-off position (IP-TP) and take-off position to highest position (TPHP). Results collected from depth camera-based marker less motion analysis system were then compared with results obtained from a conventional video based 2-D motion analysis system. Results indicated that there were significant differences between the two analysis methods in measuring the kinematic properties in both lower (knee joint) and upper (shoulder joint) extremity body movements (p < 0.05). It was also found that a lower correlation between these two analysis methods was more obvious for the knee joint movement [38.61% matched, r = 0.12 (IP-TP) and r =0.01 (TP-HP)] compared to the shoulder joint movement [61.40% matched, r =0.10 (IP-TP) and r =0.11 (TP-HP)].
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Ismail, S.I., Osman, E., Sulaiman, N., Adnan, R. (2016). Comparison between Marker-less Kinect-based and Conventional 2D Motion Analysis System on Vertical Jump Kinematic Properties Measured from Sagittal View. In: Chung, P., Soltoggio, A., Dawson, C., Meng, Q., Pain, M. (eds) Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS). Advances in Intelligent Systems and Computing, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-319-24560-7_2
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DOI: https://doi.org/10.1007/978-3-319-24560-7_2
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