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.

翻译：图像异常检测是一个热门的研究方向，随着计算技术的快速发展，近年来涌现出许多方法。利用人工智能进行图像异常检测已得到广泛研究。通过分析运动员姿态与动作图像，可以预测其损伤状态并建议必要的调整。现有方法大多依赖卷积网络从无关像素数据中提取信息，限制了模型的准确性。本文提出了一种结合残差网络（ResNet）与双向门控循环单元（BiGRU）的网络，该网络能够通过分析视频图像中肌肉与骨骼姿态的变化，预测潜在的损伤类型并提供早期预警。针对该网络复杂度较高的问题，采用麻雀搜索算法进行优化。在四个数据集上进行的实验表明，相较于其他模型，我们的模型在图像异常检测中误差最小，表现出较强的适应性。这为图像异常检测与预测分析提供了一种新途径，有助于人类健康与运动表现的可持续发展。