swell: smart reasoning systems for well-being
Funded by: COMMIT
Period: Jan, 2011 - Dec, 2016
Partners: TNO, Philips, Ericsson, Noldus, Novay, Radboud Universiteit Nijmegen, Roessingh Research and Development, NCSI
Managing work and family responsibilities is often difficult and impacts the health and well-being of employees, their families, and the workplace performance. Supporting these people in deploying a healthier life style as well as facilitating their ability to work more flexible is considered important, especially in times of economic uncertainty. On of the key aspects in deploying a healthier life style is supporting people in becoming more active because a sedentary lifestyle is one of the main risk factors for all kind of health problems such as cardiovascular diseases, diabetes and musculoskeletal problems and because of the existing evidence that being active contributes positively to feeling healthy and quality of life. Although people do recognize the need for a more active lifestyle, they often find it difficult to get started and/or to stay motivated. Furthermore, the productivity of modern knowledge workers is reaching its limits. Scientific and technological advances in sensing, machine learning, pervasive computing and user-centered design enable a series of applications that can support the user in staying or becoming healthy and work efficiently and with pleasure.
The objective of this project is to develop user-centric sensing and reasoning techniques that help to improve physical well-being (mostly in a private context) and to improve well-working (in a work context). Techniques will be deployed in personal digital assistants for a rehabilitation and nomadic work scenario giving feedback and context sensitive recommendations in order to help to achieve personalized targets related to health, well-being or efficiency. The impact on society will be a decreased risk on welfare diseases, a better work-life balance, and increased productivity due to efficient working, facilitation of an older workforce, fewer sick leaves, and a reduction of commuting travel. The activity, health status and information access patterns of an individual will be monitored by a series of sensors. The resulting model, which will be continuously learning from and adapting to and individual , can subsequently be used to provide input for an unobtrusive coach or assistant based on robust reasoning techniques, thereby increasing the individual’s sense of feeling in control.