Attitude Monitoring Algorithm for Volleyball Sports Training Based on Machine Learning in the Context of Artificial Intelligence

With the development of artificial intelligence technology and information technology, the posture of volleyball training is becoming increasingly strict. By analyzing the dynamic training posture monitoring algorithm, the posture information of the human body can be directly obtained, which enables more efficient management of volleyball sports training. This paper aims to study how to monitor volleyball training posture and give suggestions based on machine learning in the context of artificial intelligence. The traditional method of manually detecting volleyball training postures is too subjective and cannot be used to judge the movements. Therefore, this paper proposes an algorithm for human posture monitoring and studies human posture recognition. Human gesture recognition has been widely used in many fields. The experimental results in this paper show that the corrected serve deviation rate of five volleyball players is 13.1% at the highest and 11.3% at the lowest after the traditional manual visual monitoring. The highest error is 0.70 m and the lowest is 0.63 m. The overall error is high. The corrected service deviation rate of the machine learning-based attitude monitoring algorithm is 3.5% at the highest and 2.7% at the lowest. The highest error is 0.24 m and the lowest is 0.19 m. The overall error is much lower than the former. This also shows that the posture monitoring algorithm based on machine learning can effectively detect the movement of volleyball players. This enables athletes to correct their mistakes in a timely manner, improve training efficiency, and improve their own strength.