Analysis and Research on Human Movement in Sports Scene

Abstract

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.

Analysis of Children’s Sports Heuristic Teaching Based on Deep Learning

“….,ln), lj, j = 1,. . .,n represent the limb labels during the rope skipping process, and each record in D is associated with multiple labels in L. Multi-label pose analysis can be represented by a tuple (r i , Y i ), where Y i is contained in L. Our goal is to design and implement a label classification model that judges the limb labels Y i during rope skipping based on the new pose dataset ri′ [22][23][24].…”

Sports Video Classification Framework Using Enhanced Threshold Based Keyframe Selection Algorithm and Customized CNN on UCF101 and Sports1-M Dataset

“…Te working principle of spatial pyramid pooling is shown in Figure3. Figure3may be analyzed using SPP, which has a number of pooling layer scales that can be applied to convolutional layer features of any size and ultimately produce eigenvectors with fxed dimensions[18].3.1.5. Fully-Connected Layer.…”