In recent years, there has been a growing need to introduce IT technology to improve performance in professional sports and athlete training. However, while technological innovation is taking place, the amount of data that can actually be acquired is becoming enormous, and unless there are people who can provide expert advice, the compatibility with training is not high. In this study, we aimed to build a system that uses a three-dimensional accelerometer to appropriately evaluate the performance of athletes during training. We developed a classification function that detects and separates intervals from the measured time series data and automatically divides the separated sections by type of training. As a result of evaluation using data from 16 days of training for a U-18 soccer team (n=33), intervals were detected with 100% accuracy, and five types of training were detected with 80% accuracy. We confirmed that a system equipped with the developed function could speed up f eedback to athletes.
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