Motion Measurement Data Analysis based on AI Recognition Technology

Artificial intelligence identification technology shows great potential in processing sports data and improving the accuracy and efficiency of sports performance analysis. In this study, high-precision sensors and video acquisition systems are used to collect athletes' sports data in training and competition. Then, these data are preprocessed, including denoising, normalization and feature extraction. Next, the convolutional neural network (CNN) is used to classify and identify the athletes' movements. The research results show that, compared with traditional methods, the intersection and union (IoU) of AI identification technology based on CNN can reach 0.9, the minimum time efficiency can reach 27ms, and the F1 value can reach 0.91, which can more accurately identify athletes' action patterns, provide timely feedback and help optimize training plans.