Adaptive Recognition of Motion Posture in Sports Video Based on Evolution Equation. (25th September 2021)

• Record Type: Journal Article
• Authors: Yuan, Rui; Zhang, Zhendong; Le, Yanyan; Chen, Enqing
• Other Names: Chen Miaochao Academic Editor.
• Is Part Of: Advances in mathematical physics. Volume 2021(2021)
• Publication Date: 2021-09-25
• Subjects: Mathematical physics -- Periodicals; Mathematical physics; Periodicals; 530.15
• DOI: 10.1155/2021/2148062
• Languages: English
• ISSNs: 1687-9120

Abstract

Abstract : In the field of sports, the formulation of existing training plans mainly relies on the manual observation and personal experience of coaches. This method is inevitably subjective. The application of artificial intelligence technology to the training of athletes to recognize athletes' posture can help coaches assist in decision-making and greatly enhance athletes' competitive ability. The human body movements embodied in sports are more complicated, and the accurate recognition of sports postures plays an active and important role in sports competitions and training. In this paper, inertial sensor technology is applied to attitude recognition in motion. First, in order to improve the accuracy of attitude calculation and reduce the noise interference in the preparation process, this article uses differential evolution algorithm to apply attitude calculation to realize multisensor data fusion. Secondly, a two-level neural network intelligent motion gesture recognition algorithm is proposed. The two-level neural network intelligent recognition algorithm effectively recognizes similar actions by splitting the traditional single-level neural network into two-level neural networks. Experiments show that the experimental method designed in this article for the posture in motion can obtain the motion information of the examinee in real time, realize the accurate extraction of individual motion data, and complete the recognition of the motion posture. The average accuracy rate can reach 98.85%. There is a certain practical value in gesture recognition.