Classification of kinematic swimming data with emphasis on resource consumption

Abstract

The collection of kinematic data with a head-worn sensor is a promising approach for swimming data analysis in the context of athlete support systems. We present a new approach of analyzing these data and describe a system that segments the lanes of a swimming session and classifies the swimming style of each lane. Special emphasis was put on the algorithm efficiency and the analysis of the resource demands to be able to port the implementation to an embedded microcontroller. For developing the system, data of twelve subjects was collected. The data incorporated two different turn styles that mark the end of a lane as well as the four main swimming styles backstroke, breaststroke, butterfly and freestyle. All turns were successfully identified from the turn detection. Our fully automatic swimming style classification reached a classification rate of 95.0%. The results from the resource consumption analysis can be used to support the decision for the embedded target hardware of a head-worn swimming training system.