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Evaluation of Sports Visualization Based on Wearable Devices
ARTICLE

, Yunnan Normal University

iJET Volume 12, Number 12, ISSN 1863-0383 Publisher: International Association of Online Engineering, Kassel, Germany

Abstract

In order to visualize the physical education classroom in school, we create a visualized movement management system, which records the student's exercise data efficiently and stores data in the database that enables virtual reality client to call. Each individual's exercise data are gathered as the source material to study the law of group movement, playing a strategic role in managing physical education. Through the combination of wearable devices, virtual reality and network technology, the student movement data (time, space, rate, etc.) are collected in real time to drive the role model in virtual scenes, which visualizes the movement data. Moreover, the Markov chain based algorithm is used to predict the movement state. The test results show that this method can quantize the student movement data. Therefore, the application of this system in PE classes can help teacher to observe the students’ real-time movement amount and state, so as to improve the teaching quality.

Citation

Wang, B. (2017). Evaluation of Sports Visualization Based on Wearable Devices. International Journal of Emerging Technologies in Learning (iJET), 12(12), 119-126. Kassel, Germany: International Association of Online Engineering. Retrieved December 15, 2018 from .

Keywords

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References

  1. [1] Guo, K. (2016). Empirical Study on Factors of Student Satisfaction in Higher Education. Revista Iberica de Sistemas e Tecnologias de Informacao, E11, 344-355.
  2. [2] Bäckström, M., Carlsson, P., Danvind, J., Koptyug, A., Sundström, D., & Tinnsten, M. (2016). A new wind tunnel facility dedicated to sports technology research and development. Procedia Engineering, 147, 62-67. Https://doi.org/10.1016/J.proeng.2016.06.190[3]Ciuti,G.,Ricotti,L.,Menciassi,A., & Dario, P. (2015). MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy. Sensors, 15(3), 6441-6468.
  3. [6] Karaman, S., Benois-Pineau, J., Dovgalecs, V., Mégret, R., Pinquier, J., André-Obrecht, R., & Dartigues, J.F. (2014). Hierarchical Hidden Markov Model in detecting activities of daily living in wearable videos for studies of dementia. Multimedia tools and applications, 69(3), 743-771. Https://doi.org/10.1007/s11042-012-1117-x[7]Pasluosta,C.F.,Gassner,H.,Winkler,J.,Klucken, J., & Eskofier, B.M. (2015). An emerging era in the management of Parkinson's disease: wearable technologies and the internet of things. IEEE journal of biomedical and health informatics, 19(6), 1873-1881.
  4. [9] Smith, D.L., Haller, J.M., Dolezal, B.A., Cooper, C.B., & Fehling, P.C. (2014). Evaluation of a wearable physiological status monitor during simulated firefighting activities. Journal of occupational and environmental hygiene, 11(7), 427-433.
  5. [11] Wang, P., Sun, L., Yang, S., Smeaton, A.F., & Gurrin, C. (2016). Characterizing everyday activities from visual lifelogs based on enhancing concept representation. Computer Vision and Image Understanding, 148, 181-192.

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