Keywords Cross-country skiing, skating technique
Abstract The aim of this thesis was (1) to develop a method for dividing cross-country skiingcycles into phases, and (2) extract and select features characterizing the change in center of pressure (CoP) during these phases. Both tasks were solved by the help of Moticon OpenGo insole data (pressure sensors + accelerometer) from athletes on diﬀerent skill levels. The accelerometer data produced extremal values which could be detected by an algorithm. The diﬀerent phases were identiﬁed through combining search areas (based on total foot pressure) with the extremal points from accelerometer data. Selected features were compared between subjects in order to reveal whether or not they could diﬀerentiate between subjects on diﬀerent skill levels. Analysis of the foot movement in cross-country skiing could facilitate a more accurate understanding of the athletes’ techniques, which might facilitate a better performance.
This study showed that it was possible to divide cycles into phases by the use of Moticon’s OpenGo insole data. The calculated phase-features indicated that features of athletes on the same skill level had more in common than features of athletes on diﬀerent skill levels. One of the main results was that elite athletes spent a larger percentage of their cycle time on the gliding phase, than athletes on lower skill levels. Another result was that features describing the change of CoP during the gliding phase could classify ﬁve out of six athletes correctly according to skill level.