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    My name is Vincent van Hees. After graduating in Human Kinetic Technology (BEng) and Human Movement Science (MSc - cum laude) in The Netherlands I obtained my PhD in Epidemiology from the University of Cambridge in the United Kingdom.

    Since 2008, wrist worn activity monitors that store high resolution output are increasingly being used to study out of the lab human behavior. I made important contributions to this. Among other contributions: I was the first to demonstrate a correlation of this accelerometer output with human daily energy expenditure (2011), the first to systematically investigate how gravity and movement-related signal components may be separated (2013), working with others I addressed the important and universal problem of sensor calibration error that used to cause significant bias in activity monitor recordings (2014), I was the first to propose a sleep detection algorithm tailored to the strengths of modern accelerometer data (2015), and proposed a solution to the timely problem of Sleep Period Time (SPT)-window detection (2018). In parallel with the latter I broadened my software development skills (Python, R, git, SQL) and data science skills (machine learning, deep learning, statistics) at the Netherlands eScience Center as Senior Research Engineer in 2015-2019. My work as independent consultant started in March 2018, initially as an exploratory activity for one day per week. The success and the enjoyment I had of this encouraged me to become full time independent consultant as of May 2019.
    I am the creator and maintainer of open-source R package GGIR that aids the analysis of modern movement sensor data for real life human behaviour. The software translates the fundamental methodological research of the past decade into practical solutions for physical activity and sleep researchers. GGIR is widely used as witnessed by an increasing number of citations (> 120) from end-users (non-exhaustive list), and implemented in various large scale cohort, including Whitehall II Study, Pelotas Cohorts, CoLaus study, and UK Biobank, presently the largest accelerometer dataset in the world.
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    Picture of Training on accelerometer data processing and analysis (6 hours)

    Training on accelerometer data processing and analysis (6 hours)

    Do you want to analyse your own data without depending on others? Dr. van Hees (Accelting) can help you to become familiar with both the theoretical and practical aspects of analysing accelerometer data for human movement and sleep research. Dr. van Hees typically does this via online sessions, involving a combination of discussions, live demonstrations, and lectures. You are free to add your colleagues to the call.