Identifying cross country skiing techniques using power meters in ski poles

Abstract

Power meters are widely used for measuring training and racing effort in cycling, and the use of such sensors is now spreading also to other sports. Data collected from athletes’ power meters are used to help coaches analyse and understand training load, racing efforts, technique etc. In this pilot project, we have collaborated with Skisens AB, a company producing handles for cross country ski poles equipped with power meters. We have conducted a pilot study on the use of machine learning techniques on sensor data from Skisens poles to identify which sub-technique a skier is using (double poling or gears 2–4 in skating). The dataset contain labelled time-series data from three individual skiers using four different sub-techniques recorded in varied locations and varied terrain. We evaluated three machine learning models based on neural networks, with best results obtained by a LSTM network (accuracy of 95% correctly classified strokes), when a subset of data from all three skiers was used for training. As expected, accuracy dropped to 78% when the model was trained on data from only two skiers and tested on the third.