Joy Egede

Contact

• workRoom B49 Computer Science
  Jubilee Campus
  Wollaton Road
  Nottingham
  NG8 1BB
  UK
• work01159514232
• Joy.Egede@nottingham.ac.uk
• http://www.cs.nott.ac.uk/~pszje

Biography

Joy Egede is a Research Fellow in the Computer Vision Lab of the School of Computer Science. She received a First class B.Eng degree in Computer Engineering from Covenant University, Nigeria and went on to work as a Software Engineer at Quanteq Technology Services, Abuja for 3-years. She then proceeded to pursue a postgraduate degree at the University of Nottingham. During this time she received an MSc with distinction in Information Technology after which she was awarded the Horizon Digital Economy IDIC scholarship in 2014 to pursue a PhD research. She completed her PhD programme in 2018 and joined the Computer Vision Lab as a Research Fellow. She is also the recipient of the prestigious 2020 L'oréal-UNESCO For Women in Science Fellowship Award in Computing and Mathematics, for her research on multi-modal neonatal infant pain assessment using machine-assisted methods.

Research Summary

Dr Egede's research focuses on understanding human behaviour via analysis of audio-visual and biosignals using machine learning and computer vision techniques.

Previously, her PhD thesis explored the application of traditional and deep-learned algorithms to continuous pain estimation in adults and neonates with a primary focus on the analysis of facial expression changes in response to pain stimuli. This work also led to the creation of the large-scale Acute Pain in Neonates (APN_db) database aimed at supporting research in automatic neonatal pain assessment.

Currently, she works with the Biomedical Research Center in developing automated methods for detecting and interpreting medical conditions from expressive human behaviour as well as designing models for user interfaces that adapt content delivery based on social signals read off the user. This includes projects relating to the automatic objective assessment of comorbid mental health issues and pain, and using virtual agents to deliver health advice to mothers and mothers-to-be in sub-Saharan Africa.

Recent Publications

• EGEDE, J., VALSTAR, M., TORRES, M.T and SHARKEY, D., 2019. Automatic Neonatal Pain Estimation: An Acute Pain in Neonates Database. In: 8th International Conference on Affective Computing and Intelligent Interaction (ACII). 1-7
• JAISWAL, SHASHANK, EGEDE, JOY and VALSTAR, MICHEL, 2018. Deep Learned Cumulative Attribute Regression In: Automatic Face & Gesture Recognition (FG 2018), 2018 13th IEEE International Conference on. 715-722
• EGEDE, JOY, VALSTAR, MICHEL and MARTINEZ, BRAIS, 2017. Fusing deep learned and hand-crafted features of appearance, shape, and dynamics for automatic pain estimation In: Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on. 689-696
• EGEDE, JOY O and VALSTAR, MICHEL, 2017. Cumulative attributes for pain intensity estimation In: Proceedings of the 19th ACM International Conference on Multimodal Interaction. 146-153

• View all publications

• EGEDE, J., VALSTAR, M., TORRES, M.T and SHARKEY, D., 2019. Automatic Neonatal Pain Estimation: An Acute Pain in Neonates Database. In: 8th International Conference on Affective Computing and Intelligent Interaction (ACII). 1-7
• JAISWAL, SHASHANK, EGEDE, JOY and VALSTAR, MICHEL, 2018. Deep Learned Cumulative Attribute Regression In: Automatic Face & Gesture Recognition (FG 2018), 2018 13th IEEE International Conference on. 715-722
• EGEDE, JOY, VALSTAR, MICHEL and MARTINEZ, BRAIS, 2017. Fusing deep learned and hand-crafted features of appearance, shape, and dynamics for automatic pain estimation In: Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on. 689-696
• EGEDE, JOY O and VALSTAR, MICHEL, 2017. Cumulative attributes for pain intensity estimation In: Proceedings of the 19th ACM International Conference on Multimodal Interaction. 146-153