Chair: Dr Jochen Einbeck, Co-Director (Health Data Science) in the Durham Research Methods Centre
Analysing the 100,000+ accelerometer datasets in the UK Biobank using the University of Manchester computational shared facilities
Speaker – Alex Casson, University of Manchester
Abstract – The UK Biobank contains over 100,000 records from participants who wore a wrist wearable device for a period of a week, allowing investigations into their activity patterns, sleep patterns, and more. The raw accelerometery data is more than 20 TB in size, with a large amount of meta-data also available.
This presentation will overview our computational approaches taken to analyse the entire dataset using the University of Manchester computational shared facilities, with optimizations to the storage and processing approach to accelerate the analysis. We will detail these optimizations, and our open source code for processing the data on high performance clusters and on Windows PCs.
We will also highlight some of the caveats and limitations of the dataset users should be aware of when designing their studies using it.