A population group that is often overlooked in the recent revolution of self-tracking is the group of older people. This growing proportion of the general population is often faced with increasing health issues and discomfort. In order to come up with lifestyle advice towards the elderly, we need the ability to quantify their lifestyle, before and after an intervention. This research focuses on the task of activity recognition (AR) from accelerometer data. With that aim, we collect a substantial labelled dataset of older individuals wearing multiple devices simultaneously and performing a strict protocol of 16 activities (the GOTOV dataset, N=28
is a data scientist/Ph.D. candidate in the Department of Biomedical Data Sciences, section Molecular Epidemiology of the Leiden University Medical Center in Leiden, the Netherlands. He received his master in Computer Science and Advanced Data Analytics at the Leiden Institute of Advanced Computer Science. His work focuses on healthy ageing and how the physical activity of older people is linked to parameters of metabolic health and biological age.
