  Power meters are becoming a widely used tool for measuring training and
racing effort in cycling, and are now spreading also to other sports. This
means that increasing volumes of data can be collected from athletes, with the
aim of helping coaches and athletes analyse and understanding training load,
racing efforts, technique etc. In this 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 in the use of machine
learning techniques on data from Skisens poles to identify which "gear" a skier
is using (double poling or gears 2-4 in skating), based only on the sensor data
from the ski poles. The dataset for this pilot study contained labelled
time-series data from three individual skiers using four different gears
recorded in varied locations and varied terrain. We systematically evaluated a
number of machine learning techniques 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. To achieve better generalisation to
individuals not appearing in the training set more data is required, which is
ongoing work.
