Abstract
Table tennis is a sport that demands high levels of technical proficiency and body coordination from players. Biomechanical fingerprints can provide valuable insights into players' habitual movement patterns and characteristics, allowing them to identify and improve technical weaknesses. Despite the potential, few studies have developed effective methods for generating such fingerprints. To address this gap, we propose TacPrint, a framework for generating a biomechanical fingerprint for each player. TacPrint leverages machine learning techniques to extract comprehensive features from biomechanics data collected by inertial measurement units (IMU) and employs the attention mechanism to enhance model interpretability. After generating fingerprints, TacPrint provides a visualization system to facilitate the exploration and investigation of these fingerprints. In order to validate the effectiveness of the framework, we designed an experiment to evaluate the model's performance and conducted a case study with the system. The results of our experiment demonstrated the high accuracy and effectiveness of the model. Additionally, we discussed the potential of TacPrint to be extended to other sports.
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