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Knowledge-Based System Framework for Training Long Jump Athletes Using Action Recognition

T. Kamnardsiri 1, W. Janchai 1, P. Khuwuthyakorn 1, P. Suwansrikham 1, J. Klaphajone 2, and P. Suriyachan 3
1. College of Arts, Media and Technology, Chiang Mai University
2. Department of Rehabilitation Medicine, Faculty of Medicine, Chiang Mai University
3. Faculty of Education, Chiang Mai Rajabath University
Abstract—The long jump is a part of track and field event. Also, it has been a standard event in modern Olympic Games. Athletes have to use their strength, skills and effort to make distance as far as possible from a jumping point. The long jump consists of 4 phases: run phase, take-off phase, flight phase, and landing phase. The actions in each phase effect to the flight distance. If athletes perform right actions in each phase, their performance will be significantly improved prior. In order to have right actions, they need coaching from experts. However, due to the lack of experts in the field, coaching the right actions con not be proceeded widely. In this paper, we propose a framework of an expert system for training long jump athletes by combing computer vision techniques and knowledge management theory. The expert system will capture and learn the right actions of the long jump experts in each phase. Then it will be able to analyse and coach learner/jumper based on knowledge captured from the experts.

Index Terms—action recognition, expert system, long jump, computer vision, suggestion system, knowledge-based system

Cite: T. Kamnardsiri, W. Janchai, P. Khuwuthyakorn, P. Suwansrikham, J. Klaphajoneb, and P. Suriyachan, "Knowledge-Based System Framework for Training Long Jump Athletes Using Action Recognition," Vol. 6, No. 4, pp. 182-193, November, 2015. doi: 10.12720/jait.6.4.182-193
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