2.3.2. Validation Dataset

In the validation dataset, each skier performs either all the 4 classical or skating techniques on either a flat course or a natural course simultaneously. Each of the 11 files in the validation dataset has starting and end points of the recording (labelled as 0), descending points (labelled as 9), and transition points (not labelled), which are classified as noise. The start/end and descending points are deleted from the data and the transition points are removed by manually identifying the frames corresponding to such time durations using the Xsens human model videos. The filtering of this data for cycle detection followed by resampling and arrangement into tensors is in accordance with what was performed for the training dataset. A total of 11 tensors are obtained in this manner and the dimensions of each matrix in a tensor is 333 × 51 (17 gyroscopes, 3 axes each, hence 51 columns). Table 5 provides further information about this dataset.


**Table 5.** Number of cycles of each technique for the classical and skating style performed by each skier in the validation dataset on the flat and natural course.

X: The classical style data on the natural course for skier 1 is not available.

As can be observed from Table 5, different skiers have different preferences in terms of the techniques they use on a particular course. For example, skier 1 does not perform double poling (DP) on the flat course at all whereas skier 2 performs it 26 times and skier 3 performs it 46 times (highest among all classical techniques) on the same course. However, it is clear that there is a somewhat more even distribution among the usage of skating techniques as compared to classical techniques.

On the natural course, all the skiers do not use free skate (FS) and seldom use push off (P-Off) and kick double poling (KDP). Thus, it is clear that the skiers have certain preferences on the techniques they utilize on different courses (Table 5). This preference makes this dataset highly imbalanced and a perfect validation set for validating the performance of the trained deep learning model.
