Institute of Physical Education of Chaohu University, Chaohu 238240, China.
School of Physical Education and Health, East China Normal University, Shanghai 200241, China.
Comput Intell Neurosci. 2021 Dec 23;2021:2376601. doi: 10.1155/2021/2376601. eCollection 2021.
As a whole-body sport, skipping rope plays an increasingly important role in daily life. In rope-skipping education, due to the lack of professional teachers, the training efficiency of students is low. The rope-skipping monitoring device is heavy and expensive, and the cost of labor statistics and energy consumption are high. In order to quickly analyze the movement process of students and provide correct guidance, this article implements the movement analysis method of the human body movement process. The problem of limb posture analysis in rope skipping is transformed into a multilabel classification problem, a real-time human motion analysis method based on mobile vision is proposed, and the algorithm model is verified in the rope-skipping scene. The experimental results prove that this paper proposes the improved algorithm, which achieved the expected effect. In the analysis of rope-skipping action, the choice of hyperparameters during the experiment is introduced, and it is verified that the proposed ALSTM-LSTM can solve the problem of multilabel classification in the rope-skipping process. The accuracy rate reaches 95.1%, and it can provide the best in all indicators and good performance. It is of great significance for movement analysis and movement quality evaluation during exercise.
作为一项全身运动，跳绳在日常生活中发挥着越来越重要的作用。在跳绳教育中，由于缺乏专业教师，学生的训练效率较低。跳绳监测设备笨重且昂贵，劳动力统计和能耗成本高。为了快速分析学生的运动过程并提供正确的指导，本文实现了人体运动过程的运动分析方法。将跳绳过程中肢体姿势分析的问题转化为多标签分类问题，提出了一种基于移动视觉的实时人体运动分析方法，并在跳绳场景中对算法模型进行了验证。实验结果证明，本文提出的改进算法达到了预期的效果。在跳绳动作分析中，引入了实验过程中超参数的选择，并验证了所提出的 ALSTM-LSTM 可以解决跳绳过程中的多标签分类问题。准确率达到 95.1%，在所有指标中都能达到最佳，性能良好。这对运动分析和运动质量评估具有重要意义。
