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

TL;DR

This paper introduces an automated, interpretable framework for classifying infant movements related to cerebral palsy, combining body-part segmentation, feature analysis, and visualization to improve diagnosis accuracy and interpretability.

Contribution

A novel framework that automates infant movement classification with integrated visualization, enhancing interpretability over previous manual and automated methods.

Findings

Outperforms existing classification methods in accuracy and robustness.

Provides effective visual explanations of body-part contributions.

Demonstrates potential for aiding early cerebral palsy diagnosis.

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.