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Context-aware systems that make use of sensor information to reason about their context have been proposed in many domains. However, it is still hard to design effective context-aware applications, due to the absence of suitable domain theories that consider dynamic context and associated user requirements as a precursor of system development.In this paper, we discuss a theory for the well-being domain and propose a model-driven development process that exploits the proposed theory to build effective, i.e. user-centric, context-aware applications.

Recent advances in wearable sensor technology and smartphones enable simple and affordable collection of personal analytics. This paper reflects on the lessons learned in the SWELL project that addressed the design of user-centered ICT applications for self-management of vitality in the domain of knowledge workers. These workers often have a sedentary lifestyle and are susceptible to mental health effects due to a high workload. We present the sense-reason-act framework that is the basis of the SWELL approach and we provide an overview of the individual studies carried out in SWELL. In this paper, we revisit our work on reasoning: interpreting raw heterogeneous sensor data, and acting: providing personalized feedback to support behavioural change. We conclude that simple affordable sensors can be used to classify user behaviour and heath status in a physically non-intrusive way. The interpreted data can be used to inform personalized feedback strategies. Further longitudinal studies can now be initiated to assess the effectiveness of m-Health interventions using the SWELL methods.

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Abstract-The lifestyle of the Dutch workforce is degrading. Unhealthy habits cause both physical and psychological problems, putting a strain on the individual's well-being. In order to conquer both of these, a system will be created that will coach its user to improve their lifestyle through a better diet and promoting physical activity in order to improve their feeling of well-being. However, requirements engineering is troublesome in this domain. We propose ways to conquer these requirements engineering problems using model-driven engineering techniques.

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Computing applications for among others well-being and health become increasingly advanced as a result of their sensor-based awareness of the context in which they are used. Context-aware applications have the potential of providing enriched services to their users, i.e. services that are appropriate for the context at hand. A challenge for the design of context-aware applications is to identify and develop service enrichments which are effective and useful while not being overly complex and costly. It is hard to imagine, both for the designer and end-user, all possible relevant contexts and best possible corresponding enriched services. An enriched service which is not appropriate for the context at hand can irritate or even harm the user, and (eventually) leads to avoiding the use of the service. This paper discusses a model-driven approach that incorporates domain knowledge concerning the causal relationship between context factors and human conditions. We believe that such an approach facilitates the identification and development of appropriate sensor-based context-aware services. We focus on context-aware applications for the well-being domain.