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This study aims to develop and validate an automated system for identifying skating-style cross-country subtechniques using inertial sensors. In the first experiment, the performance of a male cross-country skier was used to develop an automated identification system. In the second, eight male and seven female college cross-country skiers participated to validate the developed identification system. Each subject wore inertial sensors on both wrists and both roller skis, and a small video camera on a backpack. All subjects skied through a 3450 m roller ski course using a skating style at their maximum speed. The adopted subtechniques were identified by the automated method based on the data obtained from the sensors, as well as by visual observations from a video recording of the same ski run. The system correctly identified 6418 subtechniques from a total of 6768 cycles, which indicates an accuracy of 94.8%. The precisions of the automatic system for identifying the V1R, V1L, V2R, V2L, V2AR, and V2AL subtechniques were 87.6%, 87.0%, 97.5%, 97.8%, 92.1%, and 92.0%, respectively. Most incorrect identification cases occurred during a subtechnique identification that included a transition and turn event. Identification accuracy can be improved by separately identifying transition and turn events. This system could be used to evaluate each skier’s subtechniques in course conditions.

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This study re-evaluated the magnitude of hand propulsion (HP) in the pull and push phases of the front crawl stroke and investigated the association between the angular velocity of shoulder roll (ω) and hand propulsive lift (HP). ω was computed in the plane normal to a forward direction for 16 skilled swimmers performing the front crawl stroke at a maximal sprinting pace. HP, hand propulsive drag (HP) and HP were determined by a dynamic pressure approach. HP and HP in the pull phase were greater than in the push phase (P < 0.05) while HP in the pull phase was similar to that in the push phase. Eleven swimmers out of the 16 swimmers had a significant within-swimmers correlation between ω and HP in the push phase (P < 0.05). That is, HP increased in the push phase as the ω of rolling back to the neutral position became faster. A swimmer should use more drag for hand propulsion in the pull phase and propulsion from drag and lift equally in the push phase. Based on the relationship between ω and HP in the push phase, a possible stroke technique to enhance HP using ω is discussed.

An automatic sy stem Jor evaluating the coking coals was develojled at Nippon Steel Corporation in 1974. In this system, the reflectance oj coal is measured automatically and rapidly and then converted to the coking parameters based on the coal petrographical point qf view. It takes only 30-100 min to make 2 X 10 4-1 X 10 5 measurements oj reflectance in a sample. From the measured reflectance distribution Jar the whole maceral and vitrillite alone, the coking index are comjmted. Many applications qf the automatic system Jar coal jletrograpJ~), have been developed with satisfactory results.

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New soccer shoes have been developed by considering various concepts related to kicking, such as curving a soccer ball. However, the effects of shoes on ball behaviour remain unclear. In this study, by using a finite element simulation, we investigated the factors that affect ball behaviour immediately after impact in a curve kick. Five experienced male university soccer players performed one curve kick. We developed a finite element model of the foot and ball and evaluated the validity of the model by comparing the finite element results for the ball behaviour immediately after impact with the experimental results. The launch angle, ball velocity, and ball rotation in the finite element analysis were all in general agreement with the experimental results. Using the validated finite element model, we simulated the ball behaviour. The simulation results indicated that the larger the foot velocity immediately before impact, the larger the ball velocity and ball rotation. Furthermore, the Young's modulus of the shoe upper and the coefficient of friction between the shoe upper and the ball had little effect on the launch angle, ball velocity, and ball rotation. The results of this study suggest that the shoe upper does not significantly influence ball behaviour.

We have been constructing a swimming ability improvement support system. One of the issues to be addressed is the automatic classification of swimming styles (backstroke, breaststroke, butterfly, and front crawl). The mainstream swimming style classification technique of conventional researches is based on non-ensemble learning; in their classification, breaststroke and butterfly are mixed up with each other. To improve its generalization performance, we need to use better classifiers and more adaptive feature values than previously considered. Therefore, this research has introduced (1) random forest technique, one of ensemble learning techniques, and (2) feature values specific to breaststroke and butterfly to construct a four-swimming-style classifier that has resolved this issue. From subjects with 7 to 20 years history of swimming races, we have obtained their sensor data during swimming and have divided the data into learning data and test data. We have also converted them into feature values that represent their body motions. We have selected from those body-motion-representing feature values the important data to classify four swimming styles and feature values specific to breaststroke and butterfly. We have used the learning data to construct a swimming style classifier, and the test data to evaluate its classification accuracy. The evaluation results show that (1’) the introduction of ensemble learning has improved the mean value of F-measure for breaststroke and butterfly by 0.053, and (2’) the introduction of feature values specific to breaststroke and butterfly has improved the mean value of F-measure for breaststroke and butterfly by 0.121 as compared with (1’). The proposed swimming style classifier has performed a mean F-measure of 0.981 for the four swimming styles as well as good classification accuracies for front crawl and backstroke. Therefore, we have concluded that the swimming style classifier we have constructed has resolved the problem of mixing up breaststroke and butterfly, as well as can properly classify all different swimming styles.