Basketball activity classification can help document players' statistics, allow coaches, trainers and the medical team to quantitatively supervise players' physical exertion and optimize training strategy, and further help prevent potential injuries. Traditionally, sports activity classification was done by manual notational, or through multi-camera systems or motion sensing technology. These methods were often erroneous and limited by space. This study presents a basketball activity classification model based on Dynamic Time Warping (DTW) and body kinematic measures. Twenty participants, including 10 experienced players and 10 novice players, were involved in an experimental study. The experienced and novice players differed in their years of playing basketball. Four basketball movements, including shooting, passing, dribbling, and lay-up were classified by kinematic measures. The results indicate that the proposed model can successfully classify different basketball movements with high accuracy and efficiency. Specifically, with the resultant acceleration of the hand, this model can achieve classification precision, recall, and specificity up to 0.984, 0.983 and 0.994, respectively. Findings from this study supported the feasibility of using DTW in real-time sports activity classification and provided insights into the optimal sensor placement for basketball activity classification applications.
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