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

Journal of Advances in Information Technology Vol. 6, No. 4, November 2015 Knowledge-Based System Framework for Training Long Jump Athletes Using Action Recognition T. Kamnardsiria 1, W. Janchaia 1, P. Khuwuthyakorna 1, P. Suwansrikhama 1, and J. Klaphajoneb 2 1 College of Arts, Media and Technology, Chiang Mai University 2 Department of Rehabilitation Medicine, Faculty of Medicine, Chiang Mai University Email: {teerawat.k, worawit.j, pattaraporn.k, parinya.sg, jakkrit.k}@cmu.ac.th P. Suriyachanc Faculty of Education, Chiang Mai Rajabath University [email protected] Abstract—The long jump is a part of track and field event. The achievement of long jumping is attempt to jump as Also, it has been a standard event in modern Olympic Games. far as possible. The long jump which comprise 4 phases Athletes have to use their strength, skills and effort to make are the approach run phase, the take-off on the wood board distance as far as possible from a jumping point. The long jump consists of 4 phases: run phase, take-off phase, flight phase, the flight phase and the landing phase. Furthermore, phase, and landing phase. The actions in each phase effect to in order to make the flight distance, the biomechanical the flight distance. If athletes perform right actions in each parameters of the long jump have been determined by phase, their performance will be significantly improved Take-off speed, Take-off angle, Take-off height and prior. In order to have right actions, they need coaching Aerodynamics [2]. Moreover, Linthorne (2008) addresses from experts. However, due to the lack of experts in the field, that biomechanical principles of the long jump are the coaching the right actions con not be proceeded widely. In this paper, we propose a framework of an expert system for run-up, take-off, flight, and landing phases. These training long jump athletes by combing computer vision principles are behind the successful for the long distance techniques and knowledge management theory. The expert long jumps [3]. Therefore, long jump performance is system will capture and learn the right actions of the long considerable for constructing the long jump distance. jump experts in each phase. Then it will be able to analyse Performance of a long jumping is depended on not only and coach learner/jumper based on knowledge captured a fast horizontal velocity at the end of the run-up but also from the experts. take-off technique and landing in the sand pitch. These Index Terms—action recognition, expert system, long jump, phases of the long jump are very importance and effect to computer vision, suggestion system, knowledge-based system the distance of the long jump, especially, the run-up phase which is the first phase of the long jump. Hey (1985) states that in order to make a fast run up, most long jumps I. INTRODUCTION athletes use 16-24 running strides (around 35-55 m.) The athlete reached about 95-99 per cent of their maximum The long jump was a section of the first Olympics in sprinting speed at the end of the run-up [4]. Besides, many ancient Greece, circa 708 B.C., It had first become in the long jump athletes use a check mark at 4-6 strides before Olympic sport since 1896. It has been included in track taking-off on the wood board. Therefore, their coach can and field of the athletic sports on the local, national and easily monitor the accumulated error and also solve run-up also international levels. Long jumpers generally practice in the first phase of the long jump. The take-off phase is following the long jump principle techniques such as also one of important phase for the long jump performance. approach running, taking-off, flighting and landing to The long jump athletes have to use a suitable take-off achieve in maximum performance. Until, the world record technique. For instance, the take-off leg angle at about of the long jump distance for man stands at 8.95 m. The 60-65 degree to the horizontal [5], [6]. While, the flight record has been broken by Mike Powell of the United and landing phases are a difficult action on the air. States. The world record set held in Tokyo (1991). Because, after the take-off the athlete is in free flight, so Furthermore, since 1988 the women record was held by the all angular momentum have to be considered, for Galina Chistyakova of the Soviet at 7.52 m [1]. example, the forward angular momentum (circling the arms and legs forward). Finally, the end of the flight phase Manuscript received April 7, 2015; revised August 2, 2015. is the landing on the sand pitch. Long jumpers have to prepare their bodies for the landing by lifting their legs up © 2015 J. Adv. Inf. Technol. 182 doi: 10.12720/jait.6.4.182-193 Journal of Advances in Information Technology Vol. 6, No. 4, November 2015 and extending them in front of the body [4]. computer vision techniques in order to help for analysing According to the long jump techniques, the existing human movement and also improving athlete skills. coaching methods of long jump are monitoring during the The review of literatures above has shown feasibility to jump, and then suggesting the right actions to the long apply action recognition techniques to long jump jumpers or as a slow motion video which is a popular biomechanics training jumpers. With respect to construct method for analysing motion. Nonetheless, they have to the high performance smart trainer of the long jump. In rely on many professional coaches for suggestion this paper of losing knowledge skills, we introduce a new techniques and also to correct the wrong actions of the framework of Knowledge-Based System for long jump long jump. For example, the optimisation performance of athlete by using computer vision techniques. The goal of the long jump such as the approach run [4],[5], the take-off this work is to support the coaching of the long jump [5], [6],[7],[8],[9], the flight and landing [10], [4], [11]. athletes and assist for improving their long jump These research have been studied in the sports science techniques. Also, the system is able to transfer the long research in order to improve the long jump techniques for jump knowledge to the new generation athletes. the athlete’s long jump. Therefore, the knowledge of long The major contributions of this article are the following: jump techniques are held in professional coaches and long (i) we propose a novel framework of knowledge-based jumpers. Thus, problems in transferring the skilled trainer for improving skills of the long jump athletes; (ii) knowledge from professional coaches to the new we present a system design of the application of the generation long jumpers are to be considered. In order to computer vision techniques for analysing the make robust suggestion techniques. Computer science biomechanics of the long jump. (iii) we suggest the technology is an alternative way for helping coaches and potential of the integrating system design for athletes to practice long jump techniques correctly. recommendation athletes in order to prove their skills with Nowadays, the development of digital world with the knowledge management systems. wide spaced digital equipment enable the use of The organization of this paper are the background and technology to produce huge amount of image and merges relate works presented in Section 2. In the flowing, the the technology into many automatic applications. In the conceptual frame work of the knowledge-based trainer for computer science research, computer vision techniques long jump athletes is described first in the Section 3. In the have been employed computer vision techniques for Section 4 Knowledge-Based system is designed. Section 5 analysing the human movement, called the action the preliminary experiment and results are showed at the recognition. The action recognition aims to automatically end of the paper. Finally, Section 6 the conclusion as well analyse and interpret continuity of human movements that as the future work are determined. made by a human agent during doing of a task into actions. The typical action recognition system consists of the II. BACKGROUND AND RELATED WORK feature extraction, the action learning, the action A. Long Jump Biomechanics classification, and the action segmentation [12]. For instance, in Thomas (2001) the long jump technique in the Long jump principle has been changed over time to the transition from the approach run to the take-off using modern athletics in the mid-nineteenth century. It consists of sprinting on the runway, taking-off on the wooden Time-Continuous kinematic data has been analysed. They board, flighting through the air and then landing in a sand classified the complex movement patterns by using the pit. An excellence long jumper must be a fast sprinter, single linkage algorithm. The single linkage algorithm is strong legs, a good complex take-off, flight and landing. based on minimising the distance between two objects About 6.5-7.5 m is the best female long jumper distances, [13]. Besides, Niebles (2010) presents the framework for while the best male distances could be reached around recognition complex human activities in the video by 8.0-9.0 m. [3]. using temporal model [14]. Hsu and his colleaques (2006) studied an automatically detect the motion during a standing long jump. First of all the silhouette of the jumper from video sequence were segmented from the background for all frames. Then a GA-based search was used to find the stick model. The stick model points out the essential joints of the person [15].

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