Professor DON SHARKEY's Outputs (6)

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.

Modelling Variations in Newborn Life Support Procedure Using Colored Petri Nets (2022)
Presentation / Conference Contribution
Tan, A., Remenyte-Prescott, R., Valstar, M., & Sharkey, D. (2022, August). Modelling Variations in Newborn Life Support Procedure Using Colored Petri Nets. Presented at 32nd European Safety and Reliability Conference (ESREL 2022), Dublin, Ireland

This research is conducted to study variations in the Newborn Life Support procedure. This procedure follows an evidence-based protocol to resuscitate and stabilize newborn babies requiring assistance at birth. Errors and ineffective actions in this... Read More about Modelling Variations in Newborn Life Support Procedure Using Colored Petri Nets.

Finding Comfortable Routes for Ambulance Transfers of Newborn Infants (2020)
Presentation / Conference Contribution
Partridge, T. J., Morris, D. E., Light, R. A., Leslie, A., Sharkey, D., Crowe, J. A., & McNally, D. S. (2020, July). Finding Comfortable Routes for Ambulance Transfers of Newborn Infants. Presented at 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, Montreal, QC, Canada

Early inter-hospital ambulance transport of premature babies is associated with more severe brain injury. The mechanism is unclear, but they are exposed to excessive noise and vibration. Smart-routing may help minimise these exposure levels and poten... Read More about Finding Comfortable Routes for Ambulance Transfers of Newborn Infants.

Clinical Scene Segmentation with Tiny Datasets (2019)
Presentation / Conference Contribution
Smith, T. J., Sharkey, D., Crowe, J., & Valstar, M. (2019, October). Clinical Scene Segmentation with Tiny Datasets. Presented at 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South)

Many clinical procedures could benefit from automatic scene segmentation and subsequent action recognition. Using Convolutional Neural Networks to semantically segment meaningful parts of an image or video is still an unsolved problem. This becomes e... Read More about Clinical Scene Segmentation with Tiny Datasets.

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.