Research on Aided Judgment of Rural Sports Posture Based on Deep Learning

With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports auxiliary training, to get rid of the state of traditional sports training purely relying on experience. The article proposes a deep learning BP neural network human body posture recognition algorithm and briefly introduces the human body motion posture. The purpose of this research was to use the powerful data processing, mining, and analysis functions of deep learning to train the massive data generated in competitive sports training and apply it to competitive sports training. It is committed to promoting the accuracy and analysis of competitive sports training. Refinement provides technical guidance for athletes’ training, promotes the scientific and informatized development of competitive sports training in China, and provides some reference methods for the research and application of deep learning in competitive sports training. The article’s research results show that (1) taking a rural area as an example, we recorded the exercise postures of rural athletes in five different states: static, upstairs, downstairs, walking, and running. Comparing the recognition rate and training time of The BP neural network algorithm, ABC-BP algorithm, AFS-BP algorithm, and ABC-AFS-BP algorithm, it can be found that in terms of recognition rate, ABC-AFS-BP algorithm, AFS-BP algorithm, and ABC-BP algorithm are better than traditional BP algorithm. Among them, the recognition rate of the ABC-AFS-BP algorithm is higher than that of the ABC-BP algorithm, but it takes slightly more time than the ABC-BP algorithm. In terms of training time, the ABC-BP algorithm takes less time, but the accuracy is lower than the ABC-AFS-BP algorithm; the ABC-AFS-BP algorithm has a greater improvement in time consumption than the AFS-BP model and can guarantee the recognition rate and accuracy, and the error rate curves of the four algorithms show that after 500 iterations of the training part, the iteration error value of the ABC-AFS-BP algorithm is the smallest. (2) We evaluated sports postures of athletes from a certain rural team and concluded that bad postures will have a certain impact on the body. Among them, more than 85% of athletes in football and basketball have pelvic rotation. The problem is that football players have reached 90% of the test sample. 60% of football players and basketball players have the problem of collapsed foot. The main problem of aerobic athletes is flat back and collapsed foot. More than 90% of badminton players have high and low shoulder problems, and more than 80% of them have neck problems, which is a very serious body posture problem. (3) Detecting the flexibility experiment of the BP posture detection algorithm, compared with the traditional motion posture recording method, we tested from the three aspects of recording motion accuracy, missed detection rate, and recording time. The result shows BP posture detection. The missing detection rate of the algorithm is low, basically maintained at about 2.0, the accuracy of recording actions is relatively high, generally maintained above 98%, and the highest is 99.15%, and the recording time is short, maintained at 3–4 minutes; comparing traditional posture detection with the BP attitude detection algorithm, the missed detection rate of the algorithm is relatively high, kept at 4–6, the action accuracy is lower than that of the BP attitude detection algorithm, generally kept at about 95%, and the recording time is kept at 5–6 minutes. The posture detection algorithm is more efficient.