Lim, J.; Luo, C.; Lee, S.; Song, Y.E.; Jung, H. Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles. Sensors2024, 24, 2595.
Lim, J.; Luo, C.; Lee, S.; Song, Y.E.; Jung, H. Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles. Sensors 2024, 24, 2595.
Lim, J.; Luo, C.; Lee, S.; Song, Y.E.; Jung, H. Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles. Sensors2024, 24, 2595.
Lim, J.; Luo, C.; Lee, S.; Song, Y.E.; Jung, H. Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles. Sensors 2024, 24, 2595.
Abstract
Taekwondo has evolved from a traditional martial art into an official Olympic sport. This study introduces a novel action recognition model tailored for Taekwondo unit actions, utilizing joint motion data acquired via wearable inertial measurement unit (IMU) sensors. The utilization of IMU sensor-measured motion data facilitates the capture of the intricate and rapid movements characteristic of Taekwondo techniques. The model, underpinned by a conventional convolutional neural network (CNN)-based image classification framework, synthesizes action images to represent individual Taekwondo unit actions. These action images are generated by mapping joint motion profiles onto the RGB color space, thus encapsulating the motion dynamics of a single unit action within a solitary image. To further refine the representation of rapid movements within these images, a time-warping technique was applied, adjusting motion profiles in relation to the velocity of the action. The effectiveness of the proposed model was assessed using a dataset compiled from 40 Taekwondo experts, yielding remarkable outcomes: an accuracy of 0.998, precision of 0.983, recall of 0.982, and an F1-score of 0.982. These results underscore the time-warping technique's contribution to enhancing feature representation, as well as the proposed method's scalability and effectiveness in recognizing Taekwondo unit actions.
Keywords
Action recognition; convolution neural network; human action dataset; taekwondo
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
