Profile summary

Key skills

• Sensor data

• Accelerometry

• Human movement registration

• Signal processing

• R programming

• Measurement error reduction

Dr. Vincent van Hees

eScience Research Engineer

Profile summary

Key skills

Sensor data

Accelerometry

Human movement registration

Signal processing

R programming

Measurement error reduction

Vincent graduated in Human kinetic technology (BEng) at The Hague University of Applied Sciences and in Human movement sciences (MSc with cum laude) at the VU University in Amsterdam. In 2008 Vincent moved to England to complete a PhD in Epidemiology at the MRC Epidemiology Unit within the University of Cambridge. Vincent did a post-doc at the Institute of Cellular Medicine within Newcastle University.

Central theme of Vincent’s work has been the development of scientific software and algorithms to process data from wearable movement sensors (accelerometers). Vincent pioneered the analysis of data collected with human wrist-mounted high-resolution accelerometers that have been implemented since 2007 in population research on daily physical activity and sleep: His algorithms for movement sensor calibration, sensor wear detection, and signal aggregation are now used in almost every study that uses these sensors in the world. He released his code as open source software in R package GGIR. At the eScience Center Vincent has been working on machine learning (incl. deep learning) models for Epilepsy detection from EEG signals, emotion recognition from audio-visual data, and text. Next to his work at the eScience Center, Vincent works part-time as a freelancer to provide support service to users of his GGIR software.

Vincent joined the Netherlands eScience Center in 2015.

Related projects

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