Intelligent Training Assistance System for Sports Posture Correction Feedback Based on Artificial Intelligence Algorithms

Based on related theories such as computer technology, signal processing and pattern recognition, this article designs an innovative motion posture correction algorithm. The algorithm takes motion feedback as its core and achieves accurate error correction effects through in-depth analysis and research. This article will introduce a visual error correction strategy based on a combination of image reconstruction and trajectory planning. First, we propose a method for de-deep learning and contouring in machine learning. Secondly, MATLAB software is used to implement the neural network training process and solve problems such as artificial intelligence positioning and memory in terms of human-computer interaction functions. This article successfully implements face recognition and motion tracking functions by combining these two different methods on a mobile terminal. Finally, this article tests the performance of the auxiliary system. Test results show that the system' s correction rate for squats, barbell bends, push-ups, flat support, and running training postures is more than 78%; the timeliness of identifying wrong movement postures and feedback information is within 8 seconds; the adaptability of different movement postures is also between 81% and 89%. This means that the system can help athletes adjust their posture and reduce the risk of sports injuries.