# A Model for Using Physiological Conditions for Proactive Tourist Recommendations

## TL;DR

This paper presents a data model that leverages wearable sensor data to infer tourists' physiological states, enabling proactive and personalized activity recommendations based on health conditions.

## Contribution

It introduces a novel data model linking wearable sensor parameters to physiological states for improved tourist activity recommendations.

## Key findings

- Feasibility demonstrated with a self-quantification app
- Relations established between sensor data and physiological conditions
- Framework enables context-aware tourist recommendations

## Abstract

Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending tourist 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 then comprises 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.