Our daily affective states are a key element in the analyse is of our quality of life (QoL). Yet, to date a reliable and accurate solution for an affect analysis in our daily environments does not exist. This paper draws a research path towards a development of a Body Area Network (emoBAN) that enables long-term monitoring and analysis of affective states, as well as provision of appropriate feedback via adaptive interfaces. emoBAN can be realized with the use of unobtrusive psycho physiological measurement sensors (e.g., heart rate, respiration), supported by mobile-device based self-reports to investigate a range of affects we exhibit in our natural daily life environments. The acquired data can be automatically mined for patterns and correlated with different contexts, such that QoL could be quantified and areas of personal improvement pointed out. With the use of emoBAN, we can gain a deeper understanding of our personal experiences and resulting behaviors. The system has the potential to be exploited in many application domains and contribute to defining new research questions in disciplines ranging from behavioral to applied sciences.