Title: Recognition of starting movement correction for long distance runners based on human key point detection

Authors: Xia Zhu

Addresses: The Engineering and Technical College, Chengdu University of Technology, Leshan, 614000, Sichuan, China

Abstract: In order to improve the accuracy and effectiveness of recognition of starting motion correction for long-distance runners, a method for recognising starting motion correction for long-distance runners based on human key point detection is proposed. Adopting sparse sampling method to process and collect starting action data, introducing STI module to extract data features and fuse them. Construct a human key point detection network based on collaborative spatiotemporal attention, collect dynamic gradient information of the input data, and use the collaborative spatiotemporal attention module to obtain all joint point information to complete the recognition of starting movements of long-distance runners. The results show that the proposed method has a recognition accuracy of over 96%, a root mean square error of always 0.01, and a recognition time of 1.8 seconds, indicating that the proposed method can achieve correction and recognition of starting movements of long-distance runners.

Keywords: key points of the human body; starting movement; corrective identification; STI module; convolutional algorithm for central difference graph.

DOI: 10.1504/IJBM.2024.138225

International Journal of Biometrics, 2024 Vol.16 No.3/4, pp.300 - 316

Received: 24 May 2023
Accepted: 17 Aug 2023
Published online: 30 Apr 2024 *

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