Outputs (9)

Coloured Petri Nets-based Approach for Modelling Effects of Variation on the Reliability of the Newborn Life Support Procedure (2025)
Journal Article
Tan, A., Remenyte-Prescott, R., Egede, J., Sharkey, D., & Valstar, M. (2025). Coloured Petri Nets-based Approach for Modelling Effects of Variation on the Reliability of the Newborn Life Support Procedure. Reliability Engineering & System Safety, 260, Article 111001. https://doi.org/10.1016/j.ress.2025.111001

About 10 % of newborns need a life support procedure following birth. However, this procedure has a considerable error rate of more than 25 %, which may compromise its safety and reliability. Continuous studies to improve its performance are carried... Read More about Coloured Petri Nets-based Approach for Modelling Effects of Variation on the Reliability of the Newborn Life Support Procedure.

Enhanced Infant Movement Analysis Using Transformer-Based Fusion of Diverse Video Features for Neurodevelopmental Monitoring (2024)
Journal Article
Turner, A., & Sharkey, D. (2024). Enhanced Infant Movement Analysis Using Transformer-Based Fusion of Diverse Video Features for Neurodevelopmental Monitoring. Sensors, 24(20), Article 6619. https://doi.org/10.3390/s24206619

Neurodevelopment is a highly intricate process, and early detection of abnormalities is critical for optimizing outcomes through timely intervention. Accurate and cost-effective diagnostic methods for neurological disorders, particularly in infants,... Read More about Enhanced Infant Movement Analysis Using Transformer-Based Fusion of Diverse Video Features for Neurodevelopmental Monitoring.

An Automated Performance Evaluation of the Newborn Life Support Procedure (2024)
Presentation / Conference Contribution
Tan, A., Egede, J., Remenyte-Prescott, R., Valstar, M., & Sharkey, D. (2024, January). An Automated Performance Evaluation of the Newborn Life Support Procedure. Presented at 2024 Annual Reliability and Maintainability Symposium (RAMS), Albuquerque, NM, USA

This research is conducted to develop an automated action recognition method to evaluate the performance of the Newborn Life Support (NLS) procedure. It will be useful to find deviations in the procedure, such as missing steps and incorrect actions,... Read More about An Automated Performance Evaluation of the Newborn Life Support Procedure.

Identifying Variation in the Newborn Life Support Procedure: An Automated Method (2023)
Presentation / Conference Contribution
Tan, A., Remenyte-Prescott, R., Egede, J., Valstar, M., & Sharkey, D. (2023, September). Identifying Variation in the Newborn Life Support Procedure: An Automated Method. Presented at 33rd European Safety and Reliability Conference (ESREL 2023), Southampton, UK

This research is conducted for developing an automated method to recognize variations in the Newborn Life Support (NLS) procedure. Compliance with the NLS standard guideline is essential to prevent any adverse consequences for the newborn. Video reco... Read More about Identifying Variation in the Newborn Life Support Procedure: An Automated Method.

The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development (2023)
Journal Article
Turner, A., Hayes, S., & Sharkey, D. (2023). The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development. Sensors, 23(10), Article 4800. https://doi.org/10.3390/s23104800

Neurodevelopmental delay following extremely preterm birth or birth asphyxia is common but diagnosis is often delayed as early milder signs are not recognised by parents or clinicians. Early interventions have been shown to improve outcomes. Automati... Read More about The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development.

A modelling approach to studying variations in newborn life support procedure (2023)
Journal Article
Tan, A., Remenyte-Prescott, R., Valstar, M., & Sharkey, D. (2024). A modelling approach to studying variations in newborn life support procedure. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 238(4), 777-796. https://doi.org/10.1177/1748006X231173595

Variations in clinical practice are common. However, some variations may cause undesired consequences. Careful consideration of their causes and effects is necessary to assure the quality of healthcare delivery. A modelling approach that could captur... Read More about A modelling approach to studying variations in newborn life support procedure.

The newborn delivery room of tomorrow: emerging and future technologies (2022)
Journal Article
Batey, N., Henry, C., Garg, S., Wagner, M., Malhotra, A., Valstar, M., Smith, T., Sharkey, D., & European Society for Paediatric Research (ESPR) Neonatal Resuscitation Section Writing Group. (2024). The newborn delivery room of tomorrow: emerging and future technologies. Pediatric Research, 96(3), 586-594. https://doi.org/10.1038/s41390-022-01988-y

Advances in neonatal care have resulted in improved outcomes for high-risk newborns with technologies playing a significant part although many were developed for the neonatal intensive care unit. The care provided in the delivery room (DR) during the... Read More about The newborn delivery room of tomorrow: emerging and future technologies.

Postnatal gestational age estimation of newborns using Small Sample Deep Learning (2018)
Journal Article
Torres Torres, M., Valstar, M., Henry, C., Ward, C., & Sharkey, D. (2019). Postnatal gestational age estimation of newborns using Small Sample Deep Learning. Image and Vision Computing, 83-84, 87-99. https://doi.org/10.1016/j.imavis.2018.09.003

© 2018 A baby's gestational age determines whether or not they are premature, which helps clinicians decide on suitable post-natal treatment. The most accurate dating methods use Ultrasound Scan (USS) machines, but these are expensive, require traine... Read More about Postnatal gestational age estimation of newborns using Small Sample Deep Learning.

Small Sample Deep Learning for Newborn Gestational Age Estimation (2017)
Presentation / Conference Contribution
Torres Torres, M., Valstar, M. F., Henry, C., Ward, C., & Sharkey, D. (2017, May). Small Sample Deep Learning for Newborn Gestational Age Estimation. Presented at 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017), Washington, DC, USA

A baby’s gestational age determines whether or not they are preterm, which helps clinicians decide on suitable post-natal treatment. The most accurate dating methods use Ultrasound Scan (USS) machines, but these machines are expensive, require traine... Read More about Small Sample Deep Learning for Newborn Gestational Age Estimation.