Baker, Stephanie, Kilcullen, Meegan, and Kandasamy, Yogavijayan (2025) Utilising Cot-Side Cameras in Neonatal Intensive Care Unit for Deep Learning–Assisted General Movement Assessment. Acta Paediatrica, 115 (1). pp. 7-15.

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Abstract

Aim: Neonatal units are increasingly utilising cot-side cameras to connect parents with their infants. Combined with deep learning, video obtained through cot-side cameras could assist clinicians in conducting seamless general movement assessment (GMA) of the writhing age. Method: A literature search was conducted using PubMed, Embase and SCOPUS with the following keywords: cot-side cameras, deep learning, artificial intelligence, general movement assessment and writhing age. Results: Methods for acquiring and classifying human movement are categorised into contact, non-contact and hybrid approaches. Contact modalities typically include wearable sensors placed on the body to represent human posture, while hybrid modalities combine wearable sensors or markers with non-contact sensors. Non-contact approaches include radar-based and vision-based methods, which are the most common and accessible for motion capture, employing standard or specialised cameras to capture video data. Cot-side cameras used in neonatal clinics are primarily standard red-green-blue (RGB) devices and are the leading candidates for automated GMA. Advances in deep learning can enhance motion assessment with video data through appearance- and pose-based methods, supporting computer-aided GMA. Conclusion: Advances in deep learning can enhance the motion assessment of RGB video data, offering a scalable and non-invasive solution for computer-aided GMA that could reshape early neurodevelopmental screening.

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