Climber Behavior Modeling and Recommendation.

Sport climbing is becoming more and more popular, even among non-specialists. While new routes are built each year, both indoor and outdoor, there is no effective tool for supporting climbers to choose the most appropriate routes, either for training or simply enjoying. Route recommendation is hard and risky because a reliable evaluation of the climber’s capabilities, status and subjective difficulty perception is necessary. This can be achieved only with the exploitation of Internet of Things (IoT) sensors for the automatic recording of climbers’ activity. In this research, we want to further extend the still young research subject of activity recognition in sport climbing and combine this with new recommender systems (RSs) techniques for route suggestion. We have developed an initial solution for unobtrusively and automatically detecting climbers’ activities in a gym, and we are now connecting this information with the manual inserted diary data of climbers by means of a mobile application. We present the design and the open research questions for a system that leverages sensor data and explicit feedback to generate a climber’s profile and recommend suitable routes.