The image highlights early screening for cerebral palsy through the analysis of infant movements in video recordings. By examining body poses and motion patterns, subtle motor abnormalities can be detected with emphasis on clinically relevant regions and key movement phases, enabling accurate, interpretable, and non-invasive identification of impairments and supporting timely intervention. This study proposes TransCP-Net, a novel deep learning model that utilizes hierarchical spatiotemporal attention to analyze infant pose representations.
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