Analysis of Aerobics Auxiliary Training Based on Deep Learning

• Published in: Scientific programming Vol. 2022; pp. 1 - 7
• Main Author: Li, Can
• Format: Journal Article
• Language: English
• Published: New York Hindawi 22.03.2022; John Wiley & Sons, Inc
• Subjects: Aerobics; Algorithms; Body parts; Classrooms; Data analysis; Decomposition; Deep learning; Design; Educational technology; Human motion; Image annotation; Information technology; Iterative methods; Learning; Machine learning; Motor ability; Multimedia; Physical education; Students; Teachers; Teaching; Technology assessment
• ISSN: 1058-9244; 1875-919X; 1875-919X
• DOI: 10.1155/2022/9269988

With the in-depth integration of information technology and subject teaching, it is also an inevitable trend to apply modern information technology to aerobics teaching. In this paper, the N-best algorithm is used in the video and real-time camera in aerobics, so that the human posture parameters in a single-frame image can be estimated. By using the relative position and motion direction of each part of the human body to describe the characteristics of aerobics, the Laplace scoring method is used to reduce the dimension of the data, and the discriminant human motion feature vector with a strong local topological structure is obtained. Finally, the iterative self-organizing data analysis technology (ISODATA algorithm) is used to dynamically determine the keyframe. In the aerobics video keyframe extraction experiments, the ST-FMP model improves the recognition accuracy of nondeterministic body parts of the flexible hybrid articulated human model (FMP) by about 15 percentage points and achieves 81% keyframe extraction accuracy, which is better than the keyframe algorithms of KFE and motion block. The proposed algorithm is sensitive to human motion features and human pose and is suitable for motion video annotation review.