A Feature-based Approach for Identifying Soccer Moves using an Accelerometer Sensor

During the past decade, Human Activity Recognition (HAR) systems have been an evolving topic due to the popularity of smart devices. Recognizing soccer moves in real-time is an important research problem that has not yet been studied thoroughly in the literature. In contrast to daily physical activities, recognizing soccer moves poses a set of unique challenges, such as pattern irregularity and body positions when performing these moves. In this paper, our goal is to recognize soccer moves in real-time by utilizing accelerometer data. We explore three different feature-based algorithms: Time Series Forest, Fast Shapelets, and Bag-ofSFA-Symbols. We also examine different factors that can affect the performance of these algorithms, such as parameter tuning and accelerometer axis elimination. Additionally, we introduce a novel collaborative model consisting of the above-mentioned algorithms in a majority voting mechanism to further enhance the performance of the system. We also add a light-weight classifier to act as a tie breaker in case of disagreement between the classifiers. We experimentally choose the right parameters to reduce the training time drastically without forfeiting the level of accuracy. Our collaborative model outperforms the single model by 2% to reach 84% in accuracy with a decrease in the training time by one order of magnitude.