ITEA3 project with focus on the detection and mitigation of poor mental health conditions, such as work stress and burnout, which have not yet resulted in a diagnosed mental health disorder. The project aims at a major breakthrough in the development of software-intensive applications that combine multiple heterogeneous environmental and/or wearable data sources into actionable information for improving employees' wellbeing, engagement and performance. Mad@Work will develop truly unobtrusive, privacy-safe, appealing solutions, smoothly integrated into the work environment and appropriate for long-term use in diverse real-life settings.
Practical tools and methods to identify measure, and make work stress accumulation visible at the individual level, team level, and organisation level, while ensuring privacy and retaining data ownership with the employee.
Research on automated stress identification from computer usage with virtual sensors shows that stress does manifest in the data. However, stress symptoms are also very personal thus a generic model is not a viable approach and solution must have algorithms that can learn individual patterns (semi-supervised learning)
|Effective start/end date||1/01/20 → 30/06/23|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):