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. The effort identifying positioning information of the moving object in real time has been a issue not only in sport biomechanics but also other academic areas. In order to solve this issue, this study tried to track the movement of a pitched ball that might provide an easier prediction because of a clear focus and simple movement of the object.Machine learning has been leading the research of extracting information from continuous images such as object tracking. Though the rule-based methods in artificial intelligence prevailed for decades, it has evolved into the methods of statistical approach that finds the maximum a posterior location in the image. The development of machine learning, accompanied by the development of recording technology and computational power of computer, made it possible to extract the trajectory of pitched baseball from recorded images.We present a method of baseball tracking, based on object tracking methods in machine learning. We introduce three state-of-the-art researches regarding the object tracking and show how we can combine these researches to yield a novel engine that finds trajectory from continuous pitching images. The first research is about mean shift method which finds the mode of a supposed continuous distribution from a set of data. The second research is about the research that explains how we can find the mode and object region effectively when we are given the previous image's location of object and the region. The third is about the research of representing data into features that we can deal with. From those features, we can establish a distribution to generate a set of data for mean shift.In this paper, we combine three works to track baseball's location in the continuous image frames. From the information of locations from two sets of images, we can reconstruct the real 3-D trajectory of pitched ball. We show how this works in real pitching images.

OBJECTIVES This study was to determine the acute response of the toe-spread-out exercise(TSE) on the medial longitudinal arch height(MLAH) and the static and dynamic balance.METHODS Twenty-four healthy young males and females were randomly assigned to the exercise group(n=12) or to the control group(n=12). The exercise group performed 40 repetitions of TSE while the control group had a rest on the chair. Before and after the exercise or rest, MLAH was measured while standing. One-leg standing test was conducted on the force plate with eyes closed and open. The total distance of the center of pressure (COP) was calculated to assess the static balance. Y-balance test was performed; and the anterior, and medial/lateral posterior reach distances were measured to assess the dynamic balance.RESULTS There was an interaction between group and time for the MLAH (p<.001), and the MLAH in the exercise group increased after the exercise (2.03±1.01 mm; t=-6.930, p<.001). There was an interaction between group and time for the anterior reach distance during the Y-balance test (p=.023), and the distance in the exercise group showed a strong tendency to increase after the exercise (t=-2.104, p=.059). No interaction was found for the total distance of the COP.CONCLUSIONS The 40 repetitions of TSE increased MLAH and showed a positive effect on dynamic balance in healthy young males and females. These results suggest that TSE can be useful as a new exercise method to improve the foot arch structure and function. Further research with the longer duration of TSE training for various populations is warranted.

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