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

arXiv: https://arxiv.org/abs/2106.04966 · HTML: https://mlsanity.com/paper/2106.04966/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)

## Built on by (most cited first, 7 of 7)

- [Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks](https://mlsanity.com/paper/2204.10997/) (2022)
- [Deep learning empowered sensor fusion boosts infant movement classification](https://mlsanity.com/paper/2406.09014/) (2024)
- [CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy](https://mlsanity.com/paper/2209.02824/) (2022)
- [Weakly Supervised Online Action Detection for Infant General Movements](https://mlsanity.com/paper/2208.03648/) (2022)
- [UDF-GMA: Uncertainty Disentanglement and Fusion for General Movement Assessment](https://mlsanity.com/paper/2507.04814/) (2025)
- [Infant movement classification through pressure distribution analysis](https://mlsanity.com/paper/2208.00884/) (2022)
- [Uncertainty-Aware Federated Learning for Infant Movement Analysis](https://mlsanity.com/paper/2609.31463/) (2026)

## Abstract (arXiv)

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.