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Four Sensor positions on right hand. 

Four Sensor positions on right hand. 

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Badminton is the most favourite sport in Indonesia. Since elementary school or earlier, children play badminton in a formal class or informal games. So, there are so many kind of styles in playing badminton. In this research, the pattern of arm kinesiology while playing badminton was studied, especially smash and backhand. The right arm of human wa...

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... two of four gold medals. After that, Indonesia always won gold medals in the next five summer Olympics. This condition give a big impact for Indonesian society. The government supports the facility to play badminton. It is easy to find people playing badminton in Indonesia. Badminton was taught since elementary school in Indonesia. Even though badminton is one of the most widely sports in the world now, but the study on is still only a little. In medical, badminton is well known as a relatively low risk injury compared to other sports. Manir Arora et al., [1] were attracted by the fact that many badminton players suffer from shoulder pain despite relatively safe sport. The risk of suffering shoulder pain for both recreational and elite badminton players was over 50%. This condition happened to males and females. There is no difference for shoulder kinematics between man and female. Most of the shoulder pain in badminton was resulted by Subacromial Impingement, instability or Scapulothoracic Dyskinesia. Bob Chen et al., [2] On other hand, the research about badminton technique is also very important. Study about badminton techniques also provides a wide and deep space for researchers since it still has many problems on it. C.-C. Chang, W.-C. Hsiung in [3] investigated about the effect of video instruction to study Fox Long Serve in badminton for students. They used two types of video feedback: Instant Video Feedback Group (IVFG) and Mass Video Feedback Group (MVFG) and analyzed with ANOVAs. They concluded that using IVFG students were able to perform the video instruction in long serve skill. C-L Tsai et al., [4,5] studied the footwork in badminton using EMG signal. Rectus Femoris and Vastus Medialis had the most important role from a player touched the ground until the moment before contact with the shuttlecock. Jian Yu and Guojin Zhao [6] developed local sensory system for badminton racket. Two sensors, accelerometer and acoustic sensor, were attached on the badminton racket. Vibrating was read from the accelerometer and sound pressure was calculated from acoustic sensor. This research gave contribution to design badminton racket types. Huajin et al., [7] studied about how a professional badminton player showed a great ability to predict the direction of the hit ball. This ability had significant difference compared with the non-player as the control. This research distinguished between professional badminton player and non-player ability to construct anticipation of the ball coming. David Alder et al., [8] distinguished some types of kinematics constructed by human arm, wrist and racket while performed badminton serves. Two groups of volunteers, expert and novice, were asked to anticipate the landing location of serves based on their visual search behavior. Koon kiat Teu et al., [9] used electrogoniometer and dual Euler angles to study arm movement during the badminton smash. They provided a method to help players to effectively rotate the arm segment for producing racket head speed. Rui Jiang and Zhaonian Wang [10] using the infrared monitoring system investigated the biomechanics system of smashing motion. The standard of spiking was determined from the mechanic theory and the law of conservation energy. Many research studied about how to get a strong power in smashing. This research focused on the basic technique in badminton using direction of local Euler angle gradient. Two types of hitting shuttlecock in badminton were investigated, smash and backhand, based on the 3-dimension arm position. Human arm was segmented into by four points: shoulder, elbow, wrist and back of hand. The segments position while performing smash and backhand was modelled. The sensor in this research was developed by motion node. This is a compact sensor designed for human motion tracking. The physical size is 35 mm x 35 mm x 15 mm. The weight is 10 grams. This motion node has three sensors inside, accelerometer, gyroscope and magnetometer. This study only used gyroscope sensor. It has range s 2000o/second. The sampling rate is 100 Hz. It can measure 360o range in all three axes. The error is about 0.5 to 2o RMS. This sensors was showed by Fig.1. One professional player and one amateur player and three non-player were conducted in this research. They performed 5 times smash and five times backhand. They demonstrated smash and backhand with low speed to ensure they felt comfortable performing step by step of smash and backhand technique. In this research the back of hand movement was considered to be affected by movement of shoulder, elbow, and wrist. In this case, the shoulder was the base and the back of hand was the end-effector. So, the configuration of sensor positions was design to cover the movements from base to the end-effector. Four sensor positions on the right hand were used in this research, as shown by Fig. 2. This sensor was connected to a computer for data acquisition. The data were saved in Microsoft excel format. In order to get the pattern of smash and back hand, the position of shuttlecock relative to the human shoulder was set as fixed point. Fig. 3(a) shows the position of shuttlecock for smash. Fig 3(b) shows the position of shuttlecock for backhand. The left foot is in front of the right foot for smash. The right foot is in front of left foot for backhand. The shuttlecock was in front of the head. The normal line between the shuttlecock and the shoulder was orthogonal to the chest. The shuttlecock was hit in a straight line to the opponent field, as shown by Fig. 3(c). Motion node sensor produces 3-axis local Euler angle for each segments. The pattern of arm movement was modelled by gradient of the local Euler angle. Two types of gradients were positive gradient and negative gradient. First, the algorithm created first reference point 1 (Ref_1), first positive threshold value (Th1_+) and first negative threshold value (Th1_-). If the local Euler angle bigger than Th1_+ than the signal was labelled as + (positive gradient). If the local Euler angle smaller than Th1_- than the signal was labelled as - (negative gradient). Then, the present value of Euler angle was compared to the previous value. For positive gradient, if the present value is smaller than the previous value, than ref_2, Th2_+, and Th2_- were created. The local Euler angle was labelled negative if it was smaller than Th2_-. For negative gradient, if the present value is bigger than the previous value, than ref_2, Th2_+, and Th2_- were created. The local Euler angle was labelled positive if it was bigger than Th2_+. Fig. 4 shows the illustration of the detection of arm movement pattern as the value of local Euler angle in second. Fig. 5 is the algorithm to detect the gradient pattern of the ...

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