Xi'an International Studies University, Xi'an 710128, China.
Comput Intell Neurosci. 2022 Jul 9;2022:5794914. doi: 10.1155/2022/5794914. eCollection 2022.
The purpose is to automatically and quickly analyze whether the rope skipping actions conform to the standards and give correct guidance and training plans. Firstly, aiming at the problem of motion analysis, a deep learning (DL) framework is proposed to obtain the coordinates of key points in rope skipping. The framework is based on the OpenPose method and uses the lightweight MobileNetV2 instead of the Visual Geometry Group (VGG) 19. Secondly, a multi-label classification model is proposed: attention long short-term memory-long short-term memory (ALSTM-LSTM), according to the algorithm adaptive method in the multi-label learning method. Finally, the validity of the model is verified. Through the analysis and comparison of simulation results, the results show that the average accuracy of the improved OpenPose method is 77.8%, an increase of 3.3%. The proposed ALSTM-LSTM model achieves 96.1% accuracy and 96.5% precision. After the feature extraction model VGG19 in the initial stage of OpenPose is replaced by the lightweight MobileNetV2, the pose estimation accuracy is improved, and the number of model parameters is reduced. Additionally, compared with other models, the performance of the ALSTM-LSTM model is improved in all aspects. This work effectively solves the problems of real-time and accurate analysis in human pose estimation (HPE). The simulation results show that the proposed DL model can effectively improve students' high school entrance examination performance.
目的是自动快速分析跳绳动作是否符合标准，并给出正确的指导和训练计划。首先，针对运动分析问题，提出了一种深度学习（DL）框架来获取跳绳关键点的坐标。该框架基于 OpenPose 方法，并使用轻量级 MobileNetV2 代替视觉几何组（VGG）19。其次，提出了一种多标签分类模型：注意力长短时记忆-长短时记忆（ALSTM-LSTM），根据多标签学习方法中的算法自适应方法。最后，验证了模型的有效性。通过对模拟结果的分析和比较，结果表明，改进的 OpenPose 方法的平均准确率为 77.8%，提高了 3.3%。所提出的 ALSTM-LSTM 模型的准确率达到 96.1%，精度达到 96.5%。在初始阶段的 OpenPose 中用轻量级 MobileNetV2 替换 VGG19 后，提高了姿态估计精度，减少了模型参数数量。此外，与其他模型相比，ALSTM-LSTM 模型在各个方面的性能都有所提高。这项工作有效地解决了人体姿态估计（HPE）中实时准确分析的问题。仿真结果表明，所提出的 DL 模型可以有效提高学生的中考成绩。
