Quantitative Pose-Based Analysis of Movement Disorders in Paediatric NGLY1 and SLC13A5 Patients

Chengliang Dai, Phil Scordis, Prathyusha Teeyagura, Rayann M. Solidum, Jeff Broderick, Julia Broderick, Jane Broderick, Brenda E. Porter

Keywords: Movement disorders, pose-based analysis, NGLY1, SLC13A5, paediatrics

TL;DR: This paper is about an AI-driven pose-based analysis of movement disorders in paediatric NGLY1 and SLC13A5 patients

Abstract: Movement disorders have long relied on subjective clinical observation for diagnosis and monitoring. By contrast, modern computer vision tools such as OpenPose can turn video recordings into precise, time-resolved measurements of a patient’s posture and movement. In this work, we apply a fully markerless, pose-based pipeline to classify abnormal movements in children with NGLY1 or SLC13A5 mutations. Our primary focus is on simple, physician-informed pose features that can be interpreted in clinical terms and used with conventional classifiers (Random Forest, SVM, etc.) on a very small dataset. We show that these handcrafted features capture clinically meaningful differences between movement-disorder phenotypes and can already achieve useful classification performance. In addition, we include an exploratory comparison with a transformer model that is pre-trained on large-scale action-recognition data and then fine-tuned on our pose data. This experiment illustrates the potential performance ceiling of deep learning with extensive pretraining, but we emphasise that such models are less transparent and more data-hungry than the traditional approaches that form the core contribution of this study.

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Interpretability and Explainable AI

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Submission Number: 53