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Utilising Cot-Side Cameras in Neonatal Intensive Care Unit for Deep Learning-Assisted General Movement Assessment
Stephanie Baker1, Meegan Kilcullen2, Yogavijayan Kandasamy3,4
1College of Science and Engineering, James Cook University, Cairns, Queensland, Australia.
Insights
Cot-side cameras combined with deep learning can improve general movement assessment (GMA) for infants. This technology offers a scalable, non-invasive approach for early neurodevelopmental screening.
Area of Science:
- Medical technology
- Developmental neuroscience
- Artificial intelligence in healthcare
Background:
- Neonatal units increasingly use cot-side cameras for parent-infant connection.
- General movement assessment (GMA) is crucial for evaluating infant neurodevelopment.
- Automated GMA using video analysis is a developing field.
Purpose of the Study:
- To explore the potential of cot-side cameras and deep learning for automated GMA.
- To review methods for motion acquisition and classification in neonates.
- To assess the feasibility of computer-aided GMA in clinical settings.
Main Methods:
- Literature search using PubMed, Embase, and SCOPUS.
- Keywords included: cot-side cameras, deep learning, artificial intelligence, general movement assessment, and writhing age.
- Categorization of movement acquisition methods (contact, non-contact, hybrid).
Main Results:
- Non-contact, vision-based methods using standard cameras are most accessible for motion capture.
- Cot-side cameras (RGB devices) are suitable for automated GMA.
- Deep learning enhances motion assessment from video data via appearance- and pose-based methods.
Conclusions:
- Deep learning applied to RGB video data can significantly improve motion assessment.
- This approach offers a scalable and non-invasive solution for computer-aided GMA.
- Automated GMA has the potential to transform early neurodevelopmental screening.
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.
