Detecting Anomalous Kicks in Taekwondo With Spatial and Temporal Features

A new scoring system based on information technology (IT) is an innovative way to judge matches fairly. However, there are reliability and expertise problems due to sophisticated differences in human-machine judging criteria. Taekwondo, a traditional Korean sport, uses an IT-based protector and scoring system. The system records the player’s movement data through its sensors, but it has a limitation in that it cannot detect anomalous kicks. Because anomalous kicks are executed with minimal strength outside the typical form of kick patterns, it interrupts fair judgment of the scoring system and reduces the liveliness of Taekwondo matches. To minimize the dispute by anomalous kicks, we aim to detect the anomalous kicks in Taekwondo matches. We construct a kick dataset and propose an attention-based deep learning model. The proposed model has the advantage of simultaneously handling the spatial and temporal features of the kick dataset because it is a model that combines a convolutional neural network (CNN) and bidirectional long-short term memory (BiLSTM) with an attention mechanism. The experimental results showed that the accuracy of the attention-based CNN-BiLSTM is 95.67%, and the false-positive rate is 0.086. We open our dataset and it is freely available on
https://github.com/daanVeer/Taekwondo_dataset
.