Towards incorporating affective feedback into context-aware intelligent environments

Determining the relevance of services from intelligent environments is a critical step in implementing a reliable context-aware ambient intelligent system. Designing the provision of explicit indications to the system is effective in communicating this relevance, however, such explicit indications come at the cost of user´s cognitive resources. In this work, we strive to create a novel pathway of implicit communication between the user and their ambient intelligence by employing user´s stress as a feedback pathway to the intelligent system. In addition, following a few very recent works, we propose using proven laboratory stressors to collect ground truth data for stressed states. We present results from a preliminary pilot study which shows promise for creating this implicit channel of communication as well as proves the feasibility of using laboratory stressors as a reliable method of ground truth collection for stressed states.