Gert is a postdoctoral researcher with the wearables group at the BDI. His current research focuses on genomic discovery for activity phenotypes using accelerometer data in the UK Biobank. The goal is to associate genetic variants with behavioural activity traits, which can lead to a better understanding of the underlying causes of disease and new drug targets. Gert’s research interests include activity recognition, machine learning for disease prediction and time-series analysis.
Before joining the BDI, Gert was with the Department of Engineering Science where he worked on deep learning models for foetal ECG analysis and clinical time-series data. Gert received his PhD from the University of Leuven (KU Leuven) in Belgium. His doctoral studies included work on machine learning models for food-intake monitoring and assistive technology for older adults living independently.
Statistical analysis plan: Development and validation of prediction models for new-onset atrial fibrillation in patients admitted to an intensive care unit
Bedford J., (2022)
A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography
Mertes G. et al, (2022), Sensors, 22
Measuring and Localizing Individual Bites Using a Sensor Augmented Plate During Unrestricted Eating for the Aging Population.
Mertes G. et al, (2020), IEEE journal of biomedical and health informatics, 24, 1509 - 1518
Quantifying eating behavior with a smart plate in patients with arm impairment after stroke
Mertes G. et al, (2019), 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
Measuring weight and location of individual bites using a sensor augmented smart plate.
Mertes G. et al, (2018), Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2018, 5558 - 5561