Abstract: Providing early diagnosis of cerebral palsy (CP) is key to enhancing the
developmental outcomes for those affected. Diagnostic tools such as the General
Movements 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 of
infant body movements, based upon the GMA, which unlike previous methods, also
incorporates a visualization framework to aid with interpretability. Our
proposed framework segments extracted features to detect the presence of
Fidgety Movements (FMs) associated with the GMA spatiotemporally. These
features are then used to identify the body-parts with the greatest
contribution towards a classification decision and highlight the related
body-part segment providing visual feedback to the user.
We quantitatively compare the proposed framework's classification performance
with several other methods from the literature and qualitatively evaluate the
visualization's veracity. Our experimental results show that the proposed
method performs more robustly than comparable techniques in this setting whilst
simultaneously providing relevant visual interpretability.