Research & development - Leuven | More than two weeks ago
Over the last years, several datasets on stress and mental health in diverse populations were collected at imec. Collected data types include activity, logs of feelings, sleep quality, sleep and stress annotations and physiological data from wearables (ECG signals, skin conductance, activity…). Additional to our internal development on algorithms for behaviour modeling, we are searching for novel ways to mine multi-modal datasets and uncover new insights from the data, for this proposal we want to focus on application towards sleep analysis.
In this thesis/internship, sleep quality will be assessed using movement, heart rate, electrodermal activity and skin temperature. This multitude of signals will allow for a unique approach on sleep quality analysis. Especially regarding electrodermal activity, there is a growing interest towards the appearance of so-called ‘EDA-storms’ overnight and how they relate to skin temperature increases. The student will work on multiple existing databases and is expected to apply machine learning and/or statistical modelling techniques to identify interesting patterns.
Specific thesis objectives:
Type of project: Internship, Thesis
Duration: 6-9 months
Required degree: Master of Engineering Science, Master of Science
Required background: Biomedical engineering, Computer Science, Electrotechnics/Electrical Engineering
Imec allowance will be provided for students studying at a non-Belgian university.