*3.1. Training and Validation Set Results Using Deep Learning*

The deep learning model, trained on the training data of the three skiers according to the architecture described in Section 2.4, resulted in a training dataset accuracy of at least 97.8% for the whole body, upper body, lower body, and sports biomechanics sensors configuration, and 79.4% when only the pelvis sensor is used (Table 8). Table 9 provides the confusion matrix for the training dataset for the sports biomechanics configuration.

**Table 8.** Training dataset accuracies for the five different sensor configurations.




As is clear from Table 9, the model is able to classify all the techniques almost perfectly except the classical push-off and double poling techniques. Twenty-eight push-off techniques have been wrongly classified as double poling techniques and nine double poling techniques as push-off techniques.

The validation dataset accuracies for the five different configurations of sensors are shown in Table 10.


**Table 10.** Classification accuracies for the validation dataset for five different sensor configurations.

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

The mean classification accuracy with the pelvis, the upper, and the lower body sensors is 64%, 80%, and 70% respectively, which increases to approximately 87% for the 17 sensors (the whole body) and the sports biomechanics configuration. As the accuracies while using all the 17 sensors and the five sensors in sports biomechanics configuration are the same, the sports biomechanics configuration of sensors is the optimal set due to much smaller number of sensors. The confusion matrices for the validation sets for skier 3 for the sports biomechanics configuration are shown in Tables 11–14. The confusion matrices for the validation sets of skier 1 and skier 2 can be found in Appendices A.1 and A.2.

**Table 11.** Confusion matrix for the natural course, classical style validation set of skier 3 when using the sports biomechanics configuration of sensors.



**Table 12.** Confusion matrix for the natural course, skating style validation set of skier 3 when using the sports biomechanics configuration of sensors.

**Table 13.** Confusion matrix for the flat course, classical style validation set of skier 3 when using the sports biomechanics configuration of sensors.


**Table 14.** Confusion matrix for the flat course, skating style validation set of skier 3 when using the sports biomechanics configuration of sensors.


It is interesting to note that although the model has been trained on classical and skating styles data simultaneously, it has almost perfectly learnt to differentiate between these two styles. In Table 12, three V2 techniques have been incorrectly classified as V2A, and 23 out of the 28 V2A techniques have been incorrectly classified as V1. In Table 13, five push-off techniques have been incorrectly classified as DP and 6 KDP techniques as push-off. These are certain areas of misclassification errors, which the model is not robust to. However, despite these small misclassification errors, the mean classification accuracy achieved for skier 3 is approximately 90%, which is a very high value considering that our model is trained only on the flat course data and has never observed natural course data.
