Optimization of an intelligent evaluation model for aerobics performance using artificial intelligence and multimodal deep representation

Manual aerobics scoring is sensitive to evaluator experience, while a single sensing modality cannot fully represent posture, dynamics, and musical timing. This study develops an intelligent evaluation model that encodes video, skeleton, inertial, and music-rhythm data in parallel and integrates them through cross-modal attention, reliability-aware fusion, temporal attention, and multi-task scoring. The dataset contains 8,466 valid sequences from 60 adult athletes. On a participant-independent test set, the model achieved an MAE of 3.18 points, R 2 of 0.946, and macro-F1 of 0.925 for four-class primary deviation recognition. Performance declined under cross-site and cross-device testing, with MAE increasing to 4.28 and 4.47 points, respectively. A four-week pilot involving 18 athletes and four teachers indicated shorter video-review time, but it was not designed to establish long-term technical improvement or deployment readiness. The model therefore provides an auxiliary source of quantitative evidence for training diagnosis rather than a substitute for expert judgment.