Computational Modelling of Mental Health & Well-being Data
With the dramatic increases in stress, anxiety, and mental illness in recent years, it is more important than ever to build systems that can quickly identify and intervene when an individual's health and well-being are suffering. However, collecting this type of information can be intrusive and expensive. At the same time, the general adoption of cloud-based phone and wearable technology has increased the amount and quality of data that are continuously being generated by people.
dr. Drew HendricksonPrincipal Investigator
In this research group we focus on building privacy-focused systems that can utilize the passive information generated by phones and wearable sensors to predict and understand a variety of aspects of mental health and well-being.
These digital phenotyping systems rely on statistical, machine, and deep learning methods to make actionable instantaneous predictions to help improve health and well-being.
- Tilburg IMPACT Health & Well-being – predicting student stress, sleep, and well-being using passive phone use data
- Intrusive Visual Imagery in Bipolar Disorder – evaluating the role of intrusive visual imagery in mood instability of patients with bipolar disorder
- MasterMinds: VR for safety training in industrial environments
- Geestelijke Gezondheidszorg Eindhoven
- Catharina Hospital
- Altrecht Institute for Mental Health Care
- Oxford Institute of Clinical Psychology Training
- Imperial College London
- Ghent University
- Radboud University
- Maastricht University
- Erasmus University
- Tilburg Experience Sampling Center