A novel three-axis force sensor for advanced training of shot-put athletes

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Abstract

This paper presents a novel three-axis force sensor for measuring the throwing forces of shot-put athletes. The shot-put sensor has been designed and fabricated with almost the same size and weight as the standard shot for open males. Instead of using a common shot, the shot-putters can use this shot-put sensor to make their throws. The sensor can simultaneously detect applied forces along three orthogonal directions with reasonably high accuracy. With the help of a commercially available high-speed photography system, field tests have been performed. The experimental results show that each phase of the throwing motion can be clearly identified by analysing the force curves and it is easy to distinguish between good throws and faulted throws. In this manner, the shot-put sensor serves as a powerful tool for coaches and sports scientists to make scientific researches on shot-put techniques. It also provides an intuitive and reliable guidance for the shot-put athletes to improve their skills.

Introduction

There are now increasingly intense competitions in professional sports. Sports science and scientific training are becoming more and more important for cultivating excellent athletes. Shot-put is one of the most popular athletic events in track and field. This century-old athletic event has a good effect on increasing the strength of the upper limbs and the fingers. How shot-put athletes exert force on the shot plays an important role in improving the final throwing distance. By analysing the force data, coaches and sports scientists can diagnose faulted throwing attempts and direct the shot-put athletes to improve their skills. Shot-put athlete training that depends only on the naked eyes and personal experiences of the coaches has become a thing of the past.

The recent evolution in computer-based instruments is opening the way to the research on shot-put techniques. The relationship between the angle of release and the velocity of release in shot-put was studied in [1]. High-speed videography was used to record the attempts of throwers. In [2], the constraint relations among four release variables in shot-put were investigated. A 200 Hz video motion analysis system was used to determine the initial release angle, speed, height and horizontal distance for each throw. In [3], the influence of Earth rotation on the range of the male hammer throw and shot-put was compared with that of air resistance, wind, air pressure and temperature, altitude and ground obliquity using computer modelling for typical release heights and optimal release angles.

These solutions, though effective in getting the aforementioned kinematic data, cannot provide enough kinetic data such as the throwing force of the shot-putters. In order to get the first-hand kinetic data, various sensors and measuring equipments have been developed and used in some athletic events [4], [5], [6]. A three-axis force measuring system for parallel bars has been developed in [4]. Measurement of applied force and deflection in the javelin throw has been done in [5]. A six-axis force measuring platform for weight lifting has been developed in [6]. But in the shot-put, there still lacks good methods or instruments for measuring the forces applied on the shot by athletes.

This paper describes a novel three-axis force sensor for advanced training of shot-put athletes and for research on shot-put techniques. The sensor has almost the same size and weight as the standard shot for open males and can simultaneously detect applied forces along three orthogonal directions. System overview, sensor design and some experimental results are presented in the rest of this paper.

Section snippets

System description

Fig. 1 shows the proposed force sensing system for measuring the applied forces of the shot-put athletes. It consists of two JVC9800 high-speed cameras, a flash light and a shot-put sensor. Considering that the body of the athlete will turn during the throwing motion, stereo filming method is used to record the video data. Camera 1 is mounted right behind the athlete, while camera 2 is mounted on one side of the athlete. Each camera is 15 m away from the edge of the putting circle. In this way,

Principle of sensor

The shot-put sensor is a strain gauge sensor that can simultaneously measure forces along three orthogonal directions. A 3D model of the sensing element is shown in Fig. 2. This type of structure is made of high-quality alloy structural steel. The simplified analysis model of this sensing element is a flat sheet with a rigid centre under a concentrated load, as shown in Fig. 3. In this scenario, some key mechanics parameters of the sensing element have to be calculated, such as the maximum

Experimental results

There are now two throwing techniques in shot-put, i.e. the glide technique and the spin technique. The glide technique can raise the release speed by prolonging the time during which the shot moves with the shot-putter's hand. The final throwing distance can then be improved. The spin technique has evolved from the glide technique. Rotational motions are added to the thrower's body in the spin technique. Although some elite shot-put athletes have achieved good performances by using the spin

Conclusions

We have developed a novel three-axis force sensor for measuring the forces applied on shots by shot-put athletes. The shot-put sensor has almost the same size and weight as the standard shot for open males and can simultaneously detect applied forces along three orthogonal directions. The measuring range of each component force is 100–400 N. The measuring accuracy is 2% FS. The overload capacity can reach 200% FS. With the help of a commercially available high-speed photography system, field

Acknowledgements

The authors gratefully acknowledge Prof. Yu Ge and Dr. Zhongcheng Wu (Institute of Intelligent Machines, Chinese Academy of Sciences) for enlightening advices and discussions. The authors would like to thank Wenfa Yan, Yuping Sun, Jun Ma, Fei Shen, Liankui Qiu and Min Qian (Institute of Intelligent Machines, Chinese Academy of Sciences) for providing great technological supports. This work is partially supported by NSFC Grant #60504031, China Postdoctoral Science Foundation and Jiangsu Planned

Guangming Song received the PhD degree in control science and engineering from the University of Science and Technology of China, Hefei, China, in 2004. He is presently a postdoctor of the Department of Instrument Science and Engineering, Southeast University, Nanjing, China. His current research interests include distributed measurement and control, networked sensors, and networked robots.

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Guangming Song received the PhD degree in control science and engineering from the University of Science and Technology of China, Hefei, China, in 2004. He is presently a postdoctor of the Department of Instrument Science and Engineering, Southeast University, Nanjing, China. His current research interests include distributed measurement and control, networked sensors, and networked robots.

Hongyan Yuan received the MS degree in artificial intelligence from the Institute of Intelligent Machines of Chinese Academy of Science, Hefei, China, in 2004. She is presently a PhD candidate of Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, China. Her current research interests include optical engineering, embedding system and image motion compensation.

Quanjun Song received the MS degree in artificial intelligence from the Institute of Intelligent Machines of Chinese Academy of Science, Hefei, China, in 2004. He is presently a PhD candidate of the Department of automation, University of Science and Technology, China. His current research interests include intelligent robot, biomechanics, and human–robot interaction.

Yi Tang received the PhD degree in control science and engineering from USTC (the University of Science and Technology of China), Hefei, China, in 2004. He is presently a postdoctor of the Department of Electronic Engineering and Information Science, USTC, Hefei, China. His current research interests include the Next Generation Network, Voice and Video Processing.

Yunjian Ge received his PhD degree from Institut National des Sciences Appliquées de Lyon (INSA Lyon), Lyon, France, in 1989. He is presently a professor of Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China. His current research interests include functional material, robotics and bionic perception.

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