@misc{10481/96638,
year = {2024},
month = {10},
url = {https://hdl.handle.net/10481/96638},
abstract = {The aim of this current work is to identify three different gears of cross-country skiing
utilizing embedded inertial measurement units and a suitable deep learning model. The cross-country
style studied was the skating style during the uphill, which involved three different gears: symmetric
gear pushing with poles on both sides (G3) and two asymmetric gears pushing with poles on the right
side (G2R) or to the left side (G2L). To monitor the technique, inertial measurement units (IMUs) were
affixed to the skis, recording acceleration and Euler angle data during the uphill tests performed by
two experienced skiers using the gears under study. The initiation and termination points of the tests
were controlled via Bluetooth by a smartphone using a custom application developed with Android
Studio. Data were collected on the smartphone and stored on the SD memory cards included in
each IMU. Convolutional neural networks combined with long short-term memory were utilized to
classify and extract spatio-temporal features. The performance of the model in cross-user evaluations
demonstrated an overall accuracy of 90%, and it achieved an accuracy of 98% in the cross-scene
evaluations for individual users. These results indicate a promising performance of the developed
system in distinguishing between different ski gears within skating styles, providing a valuable tool
to enhance ski training and analysis.},
organization = {Projects “SensorSportLab III”, (Redes de
Investigación en Ciencias del Deporte 2024) by Consejo Superior de Deportes (Ministerio de Cultura
y Deporte)},
organization = {Project PPJIA2023-076, funded by the program ‘Proyectos de investigación
precompetitivos para jóvenes investigadores, Modalidad A – Jóvenes Doctores,’ from the ‘Plan Propio
de Investigación 2023’ of the University of Granada},
organization = {Project IJC2020-
043307-I funded by MCIN/AEI/10.13039/501100011033},
organization = {‘European Union NextGenerationEU/
PRTR’},
publisher = {MDPI},
keywords = {inertial measurement unit (IMU)},
keywords = {deep learning},
keywords = {cross-country skiing},
title = {Gear Classification in Skating Cross-Country Skiing Using Inertial Sensors and Deep Learning},
doi = {10.3390/s24196422},
author = {Pousibet Garrido, Antonio and Polo Rodríguez, Aurora and Moreno Pérez, Juan Antonio and Ruiz García, Isidoro and Escobedo Araque, Pablo and López Ruiz, Nuria and Marcen-Cinca, Noel and Medina Quero, Javier and Carvajal Rodríguez, Miguel Ángel},
}