The main purpose of the various methods of evaluating athlete feature recognition is to monitor the current health of the athletes, thereby providing some feedback on the quality of individual training. Based on deep learning and convolutional neural networks, this paper studies athlete target recognition and proposes a feature vector extraction method based on curvature zero point. Moreover, based on the ideas of deep learning and convolutional neural networks, this paper builds an athlete feature recognition model and optimizes the algorithm. In order to verify the feasibility and efficiency of feature extraction algorithm of the sport athletes proposed by this paper and to facilitate comparison with other algorithms, this paper conducts an algorithm performance test on the sport athlete database. The research results show that the method proposed in this paper has certain advantages in the feature extraction of athletes and can be used in subsequent sports training systems.
This paper studies the remote evaluation system of tennis batting action standard based on acceleration sensor, which aims to help improve the standard degree and technical level of tennis batting action. The system includes a data acquisition module to collect original signal data of tennis batting action by the acceleration sensor signal acquisition device in the bracelet and upload to the personal computer (PC) for storage, data preprocessing module to smooth original signal data and extract the key time and frequency domain features as the evaluation basis, and remote evaluation module to assess tennis batting action standard. We applied our system to five tennis trainees from the experimental university, and the results show that the batting action standard level of student c and student e is lower. Student c is weak mainly in the best position of the hitting point and the timing of the lead shot, while student e mainly shows poor performance in the timing of movement and the stability of the overall center of gravity. Compared with the proposed system or device, our system has a short real-time delay under the concurrent use of different types of users indicating stable and high real-time evaluation performance. More importantly, our system strictly protects the user’s privacy when uploading the user’s data remotely. In short, the evaluation results obtained by our system can be used as a scientific basis to improve the tennis batting action standard.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.