Estimation of alpine skier posture using machine learning techniques

Nemec, Bojan (avtor), Petrič, Tadej (avtor), Babič, Jan (avtor), Supej, Matej (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: alpine skiing, GNSS measurements, Inertial Measurement Unit (IMU) measurements, statistical models, LWPR, neural networks
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FŠ - Fakulteta za šport
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2014
• Št. strani: Str. 18898-18914
• Številčenje: Vol. 14, iss. 10
• PID: 20.500.12556/RUL-130622
• UDK: 53
• ISSN pri članku: 1424-8220
• DOI: 10.3390/s141018898
• COBISS.SI-ID: 28015143
• Datum objave v RUL: 16.09.2021

Gradivo je del revije

• Naslov: Sensors
• Skrajšan naslov: Sensors
• Založnik: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 13.10.2014