Sun Qunqun, Wu Xiangjun
Department of Physical Education, Qiannan Normal College for Nationalities, Dunyun, Guizhou, China.
College of Sports Science, Jishou University, Jishou, Hunan, China.
PeerJ Comput Sci. 2023 Jun 27;9:e1441. doi: 10.7717/peerj-cs.1441. eCollection 2023.
There is a phenomenon of attaching importance to technique and neglecting emotion in the training of sports dance (SP), which leads to the lack of integration between movement and emotion and seriously affects the training effect. Therefore, this article uses the Kinect 3D sensor to collect the video information of SP performers and obtains the pose estimation of SP performers by extracting the key feature points. The Arousal-Valence (AV) emotion model, based on the Fusion Neural Network model (FUSNN), is also combined with theoretical knowledge. It replaces long short term memory (LSTM) with gate recurrent unit (GRU), adds layer-normalization and layer-dropout, and reduces stack levels, and it is used to categorize SP performers' emotions. The experimental results show that the model proposed in this article can accurately detect the key points in the performance of SP performers' technical movements and has a high emotional recognition accuracy in the tasks of 4 categories and eight categories, reaching 72.3% and 47.8%, respectively. This study accurately detected the key points of SP performers in the presentation of technical movements and made a major contribution to the emotional recognition and relief of this group in the training process.
在体育舞蹈训练中存在重技术轻情感的现象，这导致动作与情感缺乏融合，严重影响训练效果。因此，本文利用Kinect 3D传感器收集体育舞蹈表演者的视频信息，并通过提取关键特征点获得体育舞蹈表演者的姿态估计。基于融合神经网络模型（FUSNN）的唤醒-效价（AV）情感模型也与理论知识相结合。它用门控循环单元（GRU）代替长短期记忆（LSTM），添加层归一化和层随机失活，并减少堆叠层数，用于对体育舞蹈表演者的情感进行分类。实验结果表明，本文提出的模型能够准确检测体育舞蹈表演者技术动作表现中的关键点，在4类和8类任务中具有较高的情感识别准确率，分别达到72.3%和47.8%。本研究准确检测了体育舞蹈表演者在技术动作呈现中的关键点，为该群体在训练过程中的情感识别与缓解做出了重要贡献。
