A Basketball Training Posture Monitoring Algorithm Based on Machine Learning and Artificial Intelligence

Abstract

To increase the widespread attention of human gesture recognition technology, this paper proposes a basketball pose recognition method based on unit action division. Initially, the human gesture recognition algorithm is introduced for the verification of various effects and gestures of basketball players by monitoring various actions of basketball and to obtain the data of limbs using different detectors for different basketball movements. A large amount of data collection work was carried out for the experiment, and the corresponding experimental scenarios were described in the experimental design for testing. The methods for data processing and data division presented in this work are used to process the collected data. A feature vector set that describes a particular action is acquired and used as a sample set. The sample set is then delivered to the classifier. The classifier is implemented here based on the already-existing Weka platform, and performance evaluation and analysis of various classifiers are implemented. The results show that the differential limb function category has a better recognition effect on BP. The average accuracy of upper limb function was 92.19%, the average recall rate was 92.19%, and the accuracy of lower limb was much higher. The average accuracy of the four algorithms was within the range of 96.99% to 99.19% for lower limb movements and 84.89% to 92.19% for upper limb movements. The BP prosthetic network is used to create separate classifiers, ensuring that each basketball move was more than 95% accurate and that the average accuracy per basketball move was much more accurate. As a result, the accuracy level reached up to 98.85%. The validity of the basketball gesture recognition method recognized by the authors is sufficient and reasonable.