*2.3. Data Selection and Preprocessing*

In this study, each training instance represents one cycle of one of the techniques of either the classical or skating styles. Thus, it becomes extremely important to select the data that can represent these cyclic patterns most clearly. Figure 2 represents the typical data patterns in linear acceleration and angular velocity data for each of the 4 classical and 4 skating techniques. These figures were plotted after filtering the raw data using a low pass butterworth filter of fourth order and a cutoff frequency of 0.007 Hz. As is clear from the figure, angular velocity data, which is obtained via the gyroscope, shows more easily identifiable cyclic patterns as compared to linear acceleration data, which comes from the accelerometer. Due to the ease of identifying the cycles and low computational cost from the less sensor data, only the gyroscope data is used in this study for developing the classification models.

**Figure 2.** Comparison of the cyclic patterns in the *z*-axis (anteroposterior direction: normal to frontal plane) angular velocity and linear acceleration data (from a motion tracker on the flat surface of the shin bone of left leg) for the classical (**a**–**d**) and skating (**e**–**h**) XC- skiing techniques.
