Analyzing Body Movements for Cerebral Palsy Prediction


The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In this project, we explore the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. Given that GM assessors typically look for specific movement patterns, we attempt to model these patterns through a set of orientation-based, displacement-based and frequency-based features [McCay et al. IEEE TNSRE2022, McCay et al. EMBC2019]. Specifically we aim to model the movements associated with the assessment criteria set out in the GMA checklist, and the passive movement assessment section of the Optimality Score neurological examination. We further evaluate the performance of the pose-based features in our new deep learning architectures [Sakkos et al. IEEE Access 2021, McCay et al. IEEE Access 2020].

While machine learning-based frameworks have obtained excellent performance in a wide range of visual understanding tasks, most of the existing frameworks can be considered black-box approaches since most of the classification frameworks only output the predicted label without specifying exactly what influences the classification decision. Whilst this is acceptable in typical computer vision tasks, it is less preferable in healthcare applications, since it is essential for the clinicians to verify the prediction as well. In order to make our proposed framework more interpretable, we include a visualization module [McCay et al. BHI2021] as well as making deep learning models more interpretable [Sakkos et al. IEEE Access 2021, Zhu et al. BHI2021] by highlighting the body-parts that are contributing to the classification decision.

Readers are also referred to a closely related project on human motion analysis for healthcare applications.


The Team

Dr. Kevin D. McCay

Research Associate in AI for Healthcare, Manchester Metropolitan University

Dr. Edmond S. L. Ho

Senior Lecturer, University of Glasgow

Claire Marcroft

Neonatal Physiotherapist, Newcastle upon Tyne Hospitals, NHS Foundation Trust/Newcastle University

Patricia Dulson

Clinical Specialist Physiotherapist in Neonates, Newcastle upon Tyne Hospitals, NHS Foundation Trust

Prof. Nicholas D. Embleton

Consultant Neonatal Paediatrician and Professor of Neonatal Medicine, Newcastle upon Tyne Hospitals, NHS Foundation Trust/Newcastle University

Dr. Hubert P. H. Shum

Associate Professor, Durham University