Modeling Physiological Conditions for Proactive Tourist Recommendations

Abstract

Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to themselves and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution is a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended.