**5. Conclusions**

We utilized a novel CNN-LSTM based deep learning approach to develop a unified model for the classification of eight techniques used in classical and skating styles for XC-skiing. Overall, we achieved an accuracy of 87.2% and 95.1% on the flat and natural course test sets using the optimal sensor configuration (five gyroscope sensors: both hands, both feet, and the pelvis). High classification accuracy on both the test sets indicates that this deep learning based approach is very promising for automatic identification and classification of different XC-skiing techniques. The essence of our approach lies in eliminating the need of manually designed features required for traditional machine learning approaches and substituting the video-based and force measurement systems for classification of the XC-skiing techniques. Our model has the potential to be trained in the wild and as data of more skiers is made available, the fine tuning of the parameters will improve the accuracy as well as the scope of generalization continuously. This increases the practical value of our model and makes it suitable for real-time deployment by sports professionals. We optimized for the number of sensors and obtained the sports biomechanics configuration with five sensors as the optimal set, providing empirical evidence to researchers to base their future studies on this optimal configuration.

**Author Contributions:** Conceptualization, S.X.; Data Curation, J.J., J.K. and J.H.K.; Formal Analysis, A.A.; Funding Acquisition, Y.J.J., H.Y.K., J.H.K. and S.X.; Methodology, J.J., A.A., Y.J.J. and S.X.; Project Administration, J.J., J.K., H.Y.K. and J.H.K.; Software, A.A.; Supervision, S.X.; Writing—Original Draft, J.J. and A.A.; Writing—Review & Editing, S.X.

**Funding:** This research was funded by the Sports Science Convergence Technology Development Program of the National Research Foundation of Korea (NRF-2014M3C1B1034033) and the Basic Science Research Program of the National Research Foundation of Korea (NRF-2017R1C1B2006811).

**Acknowledgments:** The authors thank the professional skiers for their participation in the study and Liangjie Guo for technical support during field tests. We would also like to thank three anonymous reviewers for their insightful comments and efforts towards improving our manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.
