Automated Recognition and Difficulty Assessment of Boulder Routes

Abstract

Due to fast distribution of powerful, portable processing devices and wearables, the development of learning-based IoT-applications for athletic or medical usage is accelerated. But besides the offering of quantitative features, such as counting repetitions or distances, there are only a few systems which provide qualitative services, e.g., detecting malpositions to avoid injuries or to optimize training success. Therefore we present a novel, holistic, and sensor-based approach for qualitative analysis of asynchronous, non-recurrent human motion. Furthermore, we deploy it to automatically assess the difficulty level of boulder routes on basis of climbing movements. Within a comprehensive study encompassing 153 ascents of 18 climbers, we extract and examine features such as strength, endurance, and control and achieve a successful classification rate of difficulty levels of more than 98%.