論文

This paper describes optimization of the size and launch conditions of a discus. The objective function for optimization is the flight distance. Longer flight distance is better. Fourteen design variables are considered. Eight of the fourteen are concerned with the skill of the thrower. They determine the launch conditions, which are controlled by the thrower when he or she throws. The other six variables are concerned with the design of the equipment. These are the dimensions of the discus (width, thickness, radius of the metal rim and diameter of the flat center area on each side), the moment of inertia on the axis of symmetry and finally the mass of the discus. The dependences of size and the angle of attack on the aerodynamic data are estimated by using CFD (computational fluid dynamics) technique. Typical CFD results, including the effect of stalling, were confirmed by comparing the results with experimental data. As a result, the longest flight distance that could be achieved was 79 meters. In order to do this, the initial yaw rate on the axis of symmetry should be maximized. The mass should be the smallest. The moment of inertia on the axis of symmetry, the diameter of the flat center area and the width should be designed to be large. The air inflow to the discus should arrive from the upper side at the very beginning of the flight. (C) 2014 Elsevier Ltd.

The purpose of this study was to propose a new methodology for the automatic identification and the classification of the swimmers kinematical information during interval training of competitive swimming. Forty-five college swimmers attached the newly developed chest band sensor unit, which has a triple-axes accelerometer inside, and then performed a controlled interval training set with four stroke styles. The authors identified swimmer's states, such as the swimming/rest phases and the start, turn and goal touch events by using the trunk longitudinal acceleration (Ay). With the inductive inference based on the experimental results and the deductive inference based on the empirical rule on the interval training brought the estimation of the swimming time. For the classification of the swimming strokes, using the extracted swimming phase acceleration, the mean, variance and skewness of each bout were calculated. The authors compared different data mining algorithms for the stroke style classification with these descriptive statistics, such as mean, variance, skewness on the each axial acceleration as the independent variables and stroke styles as the depending variable. The accuracy of the stroke style classification by both the multi-layered neural network (NN) and the C4.5 decision tree were 91.1%. (C) 2014 Elsevier Ltd.

Ski jumping flight posture was analyzed for achieving large flight distance on the basis of high-speed video images of the initial 40 m part of 120-m ski jumping flight. The time variations of the forward leaning angle and the ski angle of attack were measured from the video images, and the aerodynamic forces were calculated from the kinematic data derived from the images. Some correlations were investigated between the initial-speed corrected flight distance and such parameters as the angles of jumper, the initial transition time and the aerodynamic force coefficients. The result indicated that small body angle of attack was a key for large flight distance in the initial phase of flight because of small drag force, and that the most distinctive fault of beginners was too large body angle of attack and ski angle of attack leading to aerodynamic stall. Too small drag force does not give an optimal condition for large flight distance because the lift force is also too small. The ratio of the lift to the drag was larger than 0.95 for advanced jumpers.

Typical in-filed technology used for human movement assessment is a single tool such as a video camera. However, there are drawbacks with this system. Understanding various human actions e. g. swimming kinematics, or the legality of a bowling action in cricket, is important for performance analysis. Inertial sensors can biomechanically capture temporal kinematics. Measuring the kinematics of human movement, allows performance analysers to monitor progression of an athlete's development. Fusing video and inertial sensor technology supplies visual feedback in conjunction with technical information. The video-sensor fusion presented here provides assessment for athletes where gross visual measures combined with fine movement monitoring possible. Therefore, a novel system can be offered which can assist athlete performance development. (C) 2012 Published by Elsevier Ltd.

Elite running typically requires performance analysis. This equally applies to able-body and amputee athletes who use prosthetic limbs. Amputee runners with artificial limbs deal with unique situations such as balance and control of the prosthetic. A new lower limb has been developed and performance requires ongoing monitoring. Inertial sensor technology, accelerometers and gyroscopes, were used to measure the limb's development, in particular stride, step, and stance duration. While research has been reported on these variables for able-body athletes, no research has investigated these kinematics from Paralympic athletes using inertial technology. The participant's existing and new prosthetic performance were compared. Performance monitoring of the limb during athlete use is required in order objectively assess the new limb's capabilities. (C) 2012 Published by Elsevier Ltd.

The swim turn makes up a substantial portion of the total swim time in competition, and efficient turns can determine finishing positions in many competitions. Swim turns can be a neglected area of performance gain. This is largely due to the difficulty in extracting performance measures that might lead to improvement. An inertial sensor was used to assess the different phases of the swimming turn and the data were then compared to the synchronised video data. As a case study, two elite swimmers, a competitive pool swimmer and an elite Triathlete, were compared as a demonstration of the technology as a potential tool for routine use. The participants were asked to perform their typical tumble turn at two velocities. Timing of the rotation relative to the turn’s push off was measured. The inertial sensor detected differences in rotation between the two swimmers tested. The sensor reported that push off occurred for the competitive swimmer before 908and for the Triathlete after 908of rotation and was confirmed by video footage. © 2011 Taylor & Francis Group, LLC. All rights reserved.

スキージャンプ飛翔中の選手の映像を側面より高速度カメラによって撮影し，その軌 道をもとにして流体力である抗力／揚力係数の変化を求めた．踏切直後の姿勢変化によって抗力／揚力の著しい違いが一流選手とそれ以外の選手間に観察された．

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Background: An algorithm for the classification of ambulatory and non-ambulatory activities using the ratio of unfiltered to filtered synthetic acceleration measured with a triaxial accelerometer and predictive models for physical activity intensity (METs) in adults and in elementary school children has been developed. The purpose of the present study was to derive predictive equations for METs with a similar algorithm in young children. Methods: Thirty-seven healthy Japanese children (four-to six-years old) participated in this study. The five non-ambulatory activities including low-intensity activities, and five ambulatory activities were selected. The raw accelerations using a triaxial accelerometer and energy expenditure by indirect calorimetry using the Douglas bag method during each activity were collected. Results: For non-ambulatory activities, especially light-intensity non-ambulatory activities, linear regression equations with a predetermined intercept (0.9) or quadratic equations were a better fit than the linear regression. The equations were different from those for adults and elementary school children. On the other hand, the ratios of unfiltered to filtered synthetic acceleration in non-ambulatory activities were different from those in ambulatory activities, as in adults and elementary school children. Conclusions: Our calibration model for young children could accurately predict intensity of physical activity including low-intensity non-ambulatory activities.

Advanced machine learning technologies are seldom applied to wearable motion sensor data obtained from sport movements. In this work, we therefore investigated neural networks for motion performance evaluation utilizing a set of inertial sensor-based ski jump measurements. A multidimensional convolutional network model that related the motion data under aspects of time, placement and sensor type was implemented. Additionally, its applicability as a measure for automatic motion style judging was evaluated. Results indicate that one multi-dimensional convolutional layer is sufficient to recognize relevant performance error representations. Furthermore, comparisons against a Support Vector Machine and a Hidden Markov Model show that the new model outperforms feature-based methods under noisy and biased data environments. Architectures such as the proposed evaluation system can hence become essential for automatic performance analysis and style judging systems in future.

In this study, a tri-axial accelerometer was attached to a flying disc to measure the kinematics during flight. A subject performed disc throwing. High speed video cameras were used to capture release and end points to validate boundary measurements. Results of the acceleration wave showed a sinusoidal pattern during the flight phase. According to the results of an additional verification experiment, we classified components of the acceleration wave during the flight phase to estimate the angular velocity of the disc, and validated it using high speed video cameras. (C) 2009 Published by Elsevier Ltd.

In this study, tri-axial accelerometer was installed in a ball to measure the acrodynamic forces during the ball's flight. The control experiments to validate were conducted besides the pitching experiment, to examine ball linear or angular velocity components independently. These were performed using a turntable and free fall test, respectively. The acceleration data of the throwing experiment showed a semi-sinusoidal wave pattern. Oil the other hand, this wave pattern was not observed in the acceleration data of both the rotational table experiment and free-fall experiment. It was revealed that the change of the orientation vectors of the gravitational acceleration and the translation acceleration on the sensor coordinate system caused the sinusoidal acceleration pattern.

Running is a cyclic motion in which each leg has two phases, stance and swing. The energy generated by the muscular forces are dominant in the phase between initial contact and mid-stance. However, during the swing phase, other internal forces act to transfer the mechanical energy between segments. In this research, we focused on the energy transfer mechanism during the swing phase and investigated how non-muscular (e. g. centrifugal and gravity) forces of each link generate, absorb, and transfer the mechanical energy. A mathematical analysis of the lower limb's movement in the swing phase of running at different speeds was carried out. A multi-body power analysis was performed using dynamical equations of a three-dimensional double pendulum to find the mechanism to increase the speed of the leg swing efficiently. Results show that the internal force at the knee joint work throughout the swing phase to generate centrifugal force in the shank and that the knee joint moment is very small in comparison to the internal force. Understanding the transfer mechanism using the internal forces could lead to economical running forms that prevent injuries and enhance the performance of the runners. (C) 2012 Published by Elsevier Ltd.