Characteristic Behavior of Human Multi-Joint Spatial Trajectory in Slalom Skiing

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 26; no. 5; pp. 801 - 807
• Main Authors: Li, Peizhang, Fei, Qing, Chen, Zhen, Yao, Xiaolan, Zhang, Yijia
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Co. Ltd 01.09.2022
• Subjects: Athletes; Behavior; Cameras; Eigenvalues; Eigenvectors; Human body; Human motion; Methods; Motion capture; Pattern analysis; Principal components analysis; Skiing; Time series; Training
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2022.p0801

The scientific analysis of the slalom training process can significantly improve the performance of athletes. In this paper, the P matrix is defined by extracting the multi-joint space coordinate trajectories of the athletes in the video to analyze the slalom training pattern. The principal component analysis was used to extract the main eigenvalues and eigenvectors of the P matrix, which were defined as the main eigenbehaviors of slalom skiing, and six main eigenbehaviors were used to achieve a similarity of 96% between the reconstructed skiing sequence and the original sequence. Similarly, the group characteristic S matrix is constructed by using the individual eigenbehaviors, and the eigenvectors of the matrix are used to define the characteristic behavior of the group to classify the hierarchical group and determine the group to which the individual belongs. Results show that this method can better identify the movement pattern of the human body’s multi-joint space trajectory in indoor or outdoor slalom skiing, and provide scientific guidance for skiing training, so that athletes can achieve better training effectiveness.