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For video-stage compensatory motion evaluation, an LSTM completely educated on the rehabilitation dataset serves as the baseline, configured as a Many-to-One model with a single layer and a hidden size of 192. The AcT, SkateFormer, and Moment models retain their authentic architectures. Both strategies generate saliency maps that emphasize key frames relevant to compensatory motion detection, even for unseen patients. This technique enables SkateFormer to prioritize key joints and frames for action recognition, effectively capturing complicated compensatory movements that may differ across duties.<br><br><br><br> Consider a monitoring system that screens VV key points (joints) on a person’s body. We can adapt this same idea to research human motion patterns captured by skeletal monitoring. 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