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.