20.500.12556/RUL-130622 Estimation of alpine skier posture using machine learning techniques High precision Global Navigation Satellite System (GNSS) measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier’s neck. A key issue is how to estimate other more relevant parameters of the skier’s body, like the center of mass (COM) and ski trajectories. Previously, these parameters were estimated by modeling the skier’s body with an inverted-pendulum model that oversimplified the skier’s body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier’s body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing. alpine skiing GNSS measurements Inertial Measurement Unit (IMU) measurements statistical models LWPR neural networks true false true Angleški jezik Ni določen Članek v reviji 2021-09-16 11:46:32 2021-09-16 11:46:32 2022-09-09 04:17:19 0000-00-00 00:00:00 2014 0 0 Str. 18898-18914 iss. 10 Vol. 14 2014 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 53 1424-8220 10.3390/s141018898 28015143 RAZ_Nemec_Bojan_2014.pdf RAZ_Nemec_Bojan_2014.pdf 1 DC0F6CAB45385C291C8B769D04D7D78C d089b4c517107cc19005c5c1008eba365cb515c92317c81f48258f3631e05e9c 00e27c92-16d3-11ec-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=147768 https://www.mdpi.com/1424-8220/14/10/18898 0 e78a70aa-16d2-11ec-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=147767 Fakulteta za šport 0 0 0