ClimbSense - Automatic Climbing Route Recognition using Wrist-worn Inertia Measurement Units

Felix Kosmalla, Florian Daiber, Antonio Krüger

In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.. ACM International Conference on Human Factors in Computing Systems (CHI-15) Crossings April 18-23 Seoul South Korea ACM 2015.


Today, sports and activity trackers are ubiquitous. Especially runners and cyclists have a variety of possibilities to record and analyze their workouts. In contrast, climbing did not find much attention in consumer electronics and human-computer interaction. If quantified data similar to cycling or running data were available for climbing, several applications would be possible, ranging from simple training diaries to virtual coaches or usage analytics for gym operators. This paper introduces a system that automatically recognizes climbed routes using wrist-worn inertia measurement units (IMUs). This is achieved by extracting features of a recorded ascent and use them as training data for the recognition system. To verify the recognition system, cross-validation methods were applied to a set of ascent recordings that were assessed during a user study with eight climbers in a local climbing gym. The evaluation resulted in a high recognition rate, thus proving that our approach is possible and operational.

ClimbSense.pdf (pdf, 4 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence