Wearable sensors, machine learning, genomics
I am an Associate Professor at the University of Oxford and lead Health Data Research UK’s national implementation project on reproducible machine learning. My research group at Oxford develops reproducible methods to analyse wearable sensor data in very large health studies to better understand the causes and consequences of disease. For example, we have developed methods to objectively measure physical activity in UK Biobank which are now actively used by researchers worldwide to demonstrate new associations with cardiovascular disease, depression, mood disorders, and others. We have also developed machine learning methods to identify sleep and functional physical activity behaviours such as walking. In addition, we have discovered the first genetic variants associated with machine-learned sensor phenotypes. This work shows the first genetic evidence that physical activity might causally lower blood pressure.
Our group has also conducted research on wearable cameras, and are interested in other wearable sensors that can help better understand the causes and consequences of disease. If you are interested in joining our research group, please do get in contact.
In 2015 I was one of only three EU Marie Curie Award winners (from ~9000 fellowship holders), selected for my contributions to health sensor data analysis. I have also contributed to the creation of guidelines on the use of mobile devices in clinical trials, in collaboration with the US Food and Drug Administration (FDA) supported Clinical Trials Transformation Initiative on “Mobile Clinical Trials”.
2015 Marie Sklodowska-Curie Actions COFUND Award (only 3 selected from ~9000 EU fellows between ’07-’13)
2015-17 British Heart Foundation Centre for Research Excellence intermediate transition fellowship
2010-13 Marie Curie postdoctoral fellowship (E.U. FP7 and Irish Health Research Board)
2005-08 Irish Research Council science PhD scholarship
GWAS identifies 14 loci for device-measured physical activity and sleep duration
DOHERTY AIDEN. et al, (2018), Nature Communications
Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.
Doherty AR. and Hollowell S., (2018), Nature Scientific Reports
Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study
Doherty AR. et al, (2017), PLoS One
Doherty AR. et al, (2013), Am J Prev Med, 44, 320 - 323