Detection and characterization of surfing events with smartphones’ embedded sensors

摘要

Surf is an increasingly popular sport, with its evaluation being mostly qualitative and subjective. Surf analysis is often performed with commercially available systems. However, most of them are based on Global Positioning System (GPS) data and/or seem to lack in the precision and validity that surf practitioners need. Thus, a novel and accurate method to detect and characterize all surf-related events during a surf session is presented, recurring to commonly available sensors present in smartphones. Identification and characterization of stationary events, such as sitting or lying when waiting for a wave, paddle and wave riding during a whole surf session was assessed recurring to inertial data alone, GPS data alone or a combination of both. The most accurate results were obtained using a combination of both sources of data with an overall event detection accuracy of 95%, encompassing all aforementioned events. In particular, wave detection showed quite promising results with 97% for both precision and recall. These findings may indicate that the proposed solution may be suitable for the creation of a precise and valid surfer performance monitor system that could be used in real-time, providing important feedback to surf practitioners.