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, a precision of 0.983, a recall of 0.982, and an F1 score of 0.982. These results underscore this time-warping technique’s contribution to enhancing feature representation, as well as the proposed method’s scalability and effectiveness in recognizing Taekwondo unit actions.

中文翻译：

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使用时间扭曲运动轮廓构建的动作图像对跆拳道单位动作进行动作识别

跆拳道已从传统武术发展成为奥运会正式比赛项目。本研究引入了一种针对跆拳道单位动作量身定制的新颖动作识别模型，利用可穿戴惯性测量单元（IMU）传感器获取的关节运动数据。利用 IMU 传感器测量的运动数据有助于捕捉跆拳道技术中复杂且快速的运动特征。该模型以传统的基于卷积神经网络 (CNN) 的图像分类框架为基础，合成动作图像来表示单个跆拳道单位动作。这些动作图像是通过将关节运动轮廓映射到 RGB 颜色空间来生成的，从而将单个单元动作的运动动态封装在单个图像中。为了进一步细化这些图像中快速运动的表示，应用了时间扭曲技术，调整与动作速度相关的运动轮廓。使用由 40 名跆拳道专家编制的数据集评估所提出模型的有效性，取得了显着的结果：准确度为 0.998，精确度为 0.983，召回率为 0.982，F1 分数为 0.982。这些结果强调了这种时间扭曲技术对增强特征表示的贡献，以及所提出的方法在识别跆拳道单位动作方面的可扩展性和有效性。