*3.4. Validation and Test Set Results Using K-Nearest Neighbors Algorithm*

To compare the results of the deep learning model with a traditional machine learning algorithm, we trained a k-nearest neighbors classifier on the training data of three subjects and tested it on the fourth subject. The feature vector corresponding to a cycle in the training data consists of the pairwise correlation values between the time-series represented by each axis of each sensor in a cycle.

Table 20 represents the validation set and Table 21 represents the test set-1 and test set-2 accuracies for all five configurations of the sensors.


**Table 20.** Classification accuracies for the validation dataset for five different sensor configurations using the k-nearest neighbors algorithm.

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

**Table 21.** Classification accuracies for test set-1 and test set-2 for the five different sensor configurations using the k-nearest neighbors algorithm.

