Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.


In this talk I will summarise what we do at the Netherlands eScience Center in Amsterdam, and I will give some examples of the projects I have been working on in Epilepsy detection, radio astronomy, and deep learning for time series. Next, I will zoom in on my work in relation to raw data accelerometry. Here, I will touch on the heuristic methods I embedded in my R package GGIR, and my current explorations to enhance these conventional methods with Python based Hidden semi-Markov models.



Vincent holds a PhD in Epidemiology from the University of Cambridge and did a post-doc at Newcastle University. Central theme of Vincent’s work has been the development of algorithms to process data from wearable movement sensors as used for population research on human behaviour. At the Netherlands eScience Center, Vincent’s current focus is on novel approaches for time series and sensor data analysis. Vincent published several journal articles on algorithms for automatic interpretation of movement sensor data, and translated his expertise in a generic open source R package GGIR (vignette, github), which has so far been used in over 20 academic publications.