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SWELL (Smart Reasoning Systems for Well-being at Work and at Home)


It is of vital importance to keep knowledge workers healthy and happy. Technology is changing our life and work styles, which can be either a blessing or a threat. There is a tendency to move less, to take fewer breaks and less time for relaxation, leading to lifestyle related health risks. Technology can be used to monitor physical and emotional well-being and help to change behavior when necessary.

The main ICT-challenge of SWELL is to develop systems that can recognize human physical activity and work tasks, and to estimate physical fitness, workload and mental strain on the basis of heterogeneous raw sensor data streams (e.g. video, posture, heart rate, computer interaction). The (securely stored) personal data is analyzed to find personal behavioral patterns leading to strain and fatigue. Finally, user centric reasoning methods are developed in order to learn effective personalized feedback strategies.

The ICT research in this project has been inspired by several technological and societal trends. The quantified self-movement is a community of people that uses sensors to capture different aspects of life in order to improve individual performance.  Life logging is the idea to make a digital capture of one’s own daily life, for instance to find patterns, trends, or to retrieve memories, artifacts in its original context. Digital profiling by e.g. Google and Facebook can be seen as partial life logs catered to improve targeted advertisement click through rates. SWELL uses, improves and integrates these technologies into user centered assistive technologies for achieving a better work/life mental/physical health balance. The user collects his own data, in order to achieve a less stressful and more balanced life, in line with the increasing societal focus on self-management.

IN 2012, SWELL has achieved results along all COMMIT/dimensions. On the science side, a large user study has been performed recording 25 knowledge workers under controlled conditions for a session of three hours under different stress conditions. The data collection of computer interaction, facial expression, posture, GSR, heart rate and self-reported emotions. An anonymous version of the dataset will be made available for research outside SWELL.

In addition SWELL technology for context learning will be included in a word-finding app for assisting people with aphasia (disturbance in formulation and comprehension of language). The plan for the app that has been developed in cooperation with the Donders Institute has been awarded with the Hersenstichting kwaliteitsprijs 2012. Finally, SWELL monitoring technology will be applied in the project MIME in the “Topsector Creative Industry”. MIME will monitor elderly interacting with television shows of their youth, increasing their well-being and increasing the associated metadata.