It is of vital importance to keep knowledge workers healthy and happy. Technology is changing our life and work styles rapidly. There is a tendency to move less, to have fewer breaks and take less time for relaxation all leading to increased societal costs due to lifestyle related health risks (e.g. burn-out). SWELL focuses to use sensor technologies to monitor physical and emotional wellbeing and help to change behavior when necessary.
The main ICT challenge of SWELL is to develop algorithms that can recognize human physical activity, identify work tasks and to estimate physical fitness, workload and mental strain on the basis of combining 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 record your daily life to identify patterns or to retrieve memories in its original context.
Whereas the digital profiling mechanisms used by giants like Google and Facebook raises serious concerns about the terms and conditions, SWELL has a fundamentally different data and business model. Users own and control their own data. SWELL uses, improves and integrates the behavioural profiling technologies into user centered assistive technologies for achieving a better belance between professional work and private life and between mental and physical health. The user collects data in order to achieve a less stressful and more balanced life, in line with the increasing societal focus on self-management.
Currently, only singe sensor monitoring systems are on the market, which have a limited potential for reflection and discovery of unhealthy life and work patterns. SWELL will eventually provide a platform that covers 24/7 life logging, with a focus on ‘what and how’ at work and physical activity and sleep / rest patterns at home and providing insight in causal relations. To our knowledge, the market does not offer such advanced integrated functionality.
Biggest results so far
Monitoring moods of workers reduces sick leaves
In 2013 TNO-research indicated that one million people of the Dutch workforce show signs of burnout and that stress is the main reason for seven percent of all sick leaves. With the goal of reducing these numbers, we have developed the Fishualization monitoring system. Fishualization enables employees to gain insights into their working habits to reduce stress and increase productivity. More.
ICT science question: how to analyze and interpret heterogeneous multi-scale sensor data? What is a reliable model to measure the state of an entire group of knowledge workers?
Involved COMMIT/partners: TNO, Radboud Nijmegen, Sense, Almende.
Coaching-app helps to find work-life balance
We have developed the SWELL e-coaching app that helps knowledge workers in achieving personal goals related to their work-life balance. The app runs on a smartphone and can access a wide variety of sensors that recognize physical activity, work activity and working tasks.More.
ICT science question: How to reason on the basis of uncertain information inputs that come from very different types of sensors? The sensors used are not proprietary developed sensors, but are off-the-shelf generic products. Currently, many users stop using feedback apps because they are not enough personalized and because the apps are too little aware of when to give feedback or not. We try to solve this problem by building a coaching app that predicts the suitable moments of feedback for each user.
Involved COMMIT/partners: Almende, Noldus, Roessingh, Sense, Philips, TNO, Innovalor.
Quantifying your life for a better well-being
Gathering objective data about every-day-life and work behaviour can help people to gain a better insight in both harmful and helpful patterns in their lives. With this aim we have developed the SWELL lifelog dashboard. More.
ICT science question: how can we unobtrusively track physical and mental well-being, both at work and at home? Which algorithms are best suited for this task? In which way do we have to display the information so that people make optimal use of the dashboard? Our approach is unique in its focus on individual users, its flexible set of different sensors, its novel algorithms.
Involved COMMIT/partners: TNO, Radboud Nijmegen, Sense, Almende