SMARTBabies - Sensing Movement using Action Recognition Technology in Babies
Introduction
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. We proposed two pose-based feature sets, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D) [McCay et al. EMBC2019], for use in our new deep learning architectures [McCay et al. IEEE Access 2020] for classifying infant movements into normal or abnormal categories.
Publications
- Kevin D. McCay, Edmond S. L. Ho, Claire Marcroft and Nicholas D. Embleton, "Establishing Pose Based Features Using Histograms for the Detection of Abnormal Infant Movements" conference, 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5469-5472, July 2019. PDF code bibtex
The Team
Claire Marcroft
Neonatal Physiotherapist, Newcastle upon Tyne Hospitals, NHS
Foundation Trust/Newcastle University
Claire.Marcroft@newcastle.ac.uk
Claire.Marcroft@newcastle.ac.uk
Prof. Nicholas D. Embleton
Consultant Neonatal Paediatrician and Professor of Neonatal Medicine, Newcastle upon Tyne Hospitals, NHS
Foundation Trust/Newcastle University
nicholas.embleton@newcastle.ac.uk
nicholas.embleton@newcastle.ac.uk
Ethical Approval
Pilot study into Human Activity Recognition and Classification Techniques for the Early Detection of Movement Difficulties in Infants (IRAS project ID: 252317, REC reference:
19/LO/0606)