*3.2. Leave-One-Out Testing Results*

To assess the generalization accuracy of the model, we perform a leave-one-out type of testing in which the flat course data of two out of the initial three skiers is used for training and the flat and natural course data of the remaining third skier is used for testing. As the third skier can be chosen in 3C1 = 3 ways, we have a total of three combinations of training and test sets. For example, combination 1 includes subject 2 and subject 3's flat course data as the training set, and subject 1's flat and natural course data as the test set. We present the results of leave-one-out testing for both the proposed deep learning model as well as a traditional k-nearest neighbors (KNN) machine learning algorithm. For the KNN algorithm, 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. For example, while using the whole-body configuration of sensors (17 sensors), there are a total of 51 time series in each cycle, which correspond to a feature vector of length 1276 (=51C2 + 1). Table 15 presents the results of the proposed deep learning model and Table 16 for the KNN machine learning model when a leave-one-out type of testing is performed using the sports biomechanics configuration of sensors. The results for the other four configurations of sensors are available in the Appendix A.3.

**Table 15.** Classification accuracies for the first three skiers using the proposed deep learning model when a leave-one-out type of testing is performed using the sports biomechanics configuration of sensors.


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

**Table 16.** Classification accuracies for the first three skiers using the k-nearest neighbors machine learning model when a leave-one-out type of testing is performed using the sports biomechanics configuration of sensors.


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

The overall mean accuracy for the leave-one-out type of testing for the three skiers using k-nearest neighbors' algorithm is ~65%, which increases to ~80% when the proposed deep learning model is used. Also, the mean accuracy values for each skier is higher in the case of the deep learning model as compared to the KNN model.
