  Image anomaly detection is a popular research direction, with many methods
emerging in recent years due to rapid advancements in computing. The use of
artificial intelligence for image anomaly detection has been widely studied. By
analyzing images of athlete posture and movement, it is possible to predict
injury status and suggest necessary adjustments. Most existing methods rely on
convolutional networks to extract information from irrelevant pixel data,
limiting model accuracy. This paper introduces a network combining Residual
Network (ResNet) and Bidirectional Gated Recurrent Unit (BiGRU), which can
predict potential injury types and provide early warnings by analyzing changes
in muscle and bone poses from video images. To address the high complexity of
this network, the Sparrow search algorithm was used for optimization.
Experiments conducted on four datasets demonstrated that our model has the
smallest error in image anomaly detection compared to other models, showing
strong adaptability. This provides a new approach for anomaly detection and
predictive analysis in images, contributing to the sustainable development of
human health and performance.
