Since the 1980s, machine learning has attracted extensive attention in the field of artificial intelligence. Following the expert system, it opened a precedent for the application of machine learning in the field of artificial intelligence and became one of the important topics of artificial intelligence. However, in the field of volleyball, the application of machine learning and information technology in volleyball is extremely limited. Volleyball has not developed widely in society nor has it become a common event in people’s daily life. Therefore, the development of volleyball in China lags behind. Unlike other sports, volleyball requires both strong skills and playing tactics. While taking into account the technical and tactical aspects, the requirements for the comprehensive quality and learning ability of both sides of the teaching are relatively strict. If the application of modern information technology is neglected, it may affect the teaching effect of volleyball and hinder the long-term spread of volleyball. The article starts with the serving, landing, and blocking of two groups of volleyball players with different sports levels. Through the application of machine learning and digital information technology in volleyball, as well as the use of artificial neural networks and genetic algorithms, the reaction time and accuracy of judging serving, landing, and blocking are improved, and specific application strategies are further proposed. According to the influence of athletes of different levels on the cognition of volleyball landing points, it can be seen that there are three parts that account for 40% of the allocation.

Medical experts and academics are progressively becoming aware of knee injuries faced by tai chi practitioners in China. This occurs when the knee is bent with weight on the foot turned during Tai Chi. We propose an enhanced convolutional neural network (CNN) technique for early warning of joint injury risk during Tai Chi exercise in this research. This improved neural network approach can detect the risk of knee joint injury in practitioners early on to aid in early precaution and treatment. A multiscale feature extraction module is developed by performing several scales of convolutional layer extraction on the input data features and then combining the results to maximize the amount of feature information included in the extracted joint data. The results revealed that in experiments on a Taiwanese Tai Chi community dataset, the proposed method had an average diagnostic accuracy greater than 90 percent, significantly higher than the average diagnostic accuracy of the comparison methods on the dataset.

Introduction: There are many methods for lower limb explosive strength training in soccer athletes, and the most common is strength gain training by load increase. There is still no consensus on whether this type of training can influence jumping performance in soccer athletes. Objective: To explore the influence of muscle gain by added weight on jumping performance and lower limb muscle strength in soccer athletes. Methods: 60 participants were equally divided between high, low, and control training groups. The load was implemented with a weight vest for eight weeks. The training frequency was 40 to 60 minutes three times a week, and the training protocols and schedules of the two groups were the same, while the control group was not involved in any sports training. Results: The isokinetic muscle strength test of the left knee extensor before and after eight weeks of training showed no significant interaction between maximum torque and time to reach maximum torque at 60°/s and 180°/s (P > 0.05). After the jump test, a significant difference appears in the main effects on time factors between group A and group B. Conclusion: Strength training by load addition is an effective training method to improve the sport’s ability in the lower limbs of soccer athletes. Evidence Level II; Therapeutic Studies - Investigating the result.

Algorithms are ubiquitous in nature and human society, and algorithms in national sports are the internal mechanisms for the creation and development of national sports. Algorithms from nature, society, and culture act as the external driving force for the development of ethnic sports. Different ethnic sports are based on physical behaviors, including physical recreation, social interaction, and life-shaping behaviors. In this paper, we suggest an algorithm for health and wellness elevation of ethnic sports in the context of body-medicine integration, examine the fall situation in sports life, and propose a bidirectional LSTM fall detection model, which can automatically extract deeper data features within the fall behavior for the input data (taken from inertial sensors) and realize the processing of data from preprocessing to detection results. Medical disciplines provide scientific ideas and pathways that are founded on a rigorous medical way of thinking and knowledge system to summarize sports, so that they can be prescribed to explore new pathways of exercise for health, to carry out deliberate, planned, and scientific exercise. Finally, the superiority of the proposed algorithm in this paper is verified on a relevant dataset.