**3. Results**

We now present results for the training, validation, and test sets obtained by training the proposed CNN-LSTM based deep learning model. To compare the results with a traditional machine learning algorithm, we also trained a k-nearest neighbor (KNN) classifier by extracting manually designed features from the training data. Five different combinations of sensors are used for training the model as shown in Table 7. We will compute and compare the results for each of the sensor configurations and come up with the best subset of the 17 sensors, which should be used for the analysis of XC-skiing techniques in future studies.


