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Providing early diagnosis of cerebral palsy (CP) is key to enhancing thedevelopmental outcomes for those affected. Diagnostic tools such as the GeneralMovements Assessment (GMA), have produced promising results in early diagnosis,however these manual methods can be laborious.  In this paper, we propose a new framework for the automated classification ofinfant body movements, based upon the GMA, which unlike previous methods, alsoincorporates a visualization framework to aid with interpretability. Ourproposed framework segments extracted features to detect the presence ofFidgety Movements (FMs) associated with the GMA spatiotemporally. Thesefeatures are then used to identify the body-parts with the greatestcontribution towards a classification decision and highlight the relatedbody-part segment providing visual feedback to the user.  We quantitatively compare the proposed framework's classification performancewith several other methods from the literature and qualitatively evaluate thevisualization's veracity. Our experimental results show that the proposedmethod performs more robustly than comparable techniques in this setting whilstsimultaneously providing relevant visual interpretability.

Towards Explainable Abnormal Infant Movements Identification: A Body-part Based Prediction and Visualisation Framework