GGIR
Converts raw data from wearables into insightful reports for researchers investigating human daily physical activity and sleep.
Detecting human sleep from wearable accelerometer data without the aid of sleep diaries
The project aims to identify genetic variants associated with sleep patterns, and to perform Mendelian randomisation studies to identify the downstream causal consequences of disturbed sleep patterns on metabolic diseases such as obesity and type 2 diatbetes. UK Biobank offers a large and high quality dataset to perform above analyses. Over a hundred thousand individuals wore a wearable movement sensor (accelerometer) on their wrist for the duration of one-week (24/7). Previously, eScience Engineer Vincent van Hees developed and published software to analyse this kind of accelerometer data. This was successfully used in preliminary analyses of UK Biobank. The sleep detection functionality of his software is designed to combine sleep diary information with accelerometer information. However, in UKBiobank there is no sleep diary data. Vincent implemented a possible solution for this but did not publish an article on it. In the project we will evaluate the currently implemented algorithm to detect human sleep from wearable accelerometer data without the aid of sleep diaries. Next, we will attempt to develop a better method, e.g. with machine learning, and release as update to the existing open source software.
Coronary artery disease: risk estimations and interventions for prevention and Early detection
Targeting key risk factors for cardiovascular disease in at-risk individuals using a personalized and adaptive approach
Self tracking for prevention and diagnosis of heart disease
Using big data to put a cardiovascular digital twin into the hands of people
Sharing TADPOLE’s algorithms for reuse and evaluation
Advancing actigraphy-based daily activity and sleep analysis with machine learning
Advancing technology for multimodal analysis of emotion expression in everyday life
Gaining insights from wearable movement sensors
Prediction models based on EEG characteristics
Converts raw data from wearables into insightful reports for researchers investigating human daily physical activity and sleep.