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Sensors 2014, 14(10), 18898-18914; doi:10.3390/s141018898

Estimation of Alpine Skier Posture Using Machine Learning Techniques

1
Department of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana 1000, Slovenia
2
Faculty of Sport, University of Ljubljana, Ljubljana 1000, Slovenia
3
Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper SI-6101, Slovenia
*
Author to whom correspondence should be addressed.
Received: 5 August 2014 / Revised: 11 September 2014 / Accepted: 17 September 2014 / Published: 13 October 2014
(This article belongs to the Section Physical Sensors)
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Abstract

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. View Full-Text
Keywords: alpine skiing; GNSS measurements; Inertial Measurement Unit (IMU) measurements; statistical models; LWPR; neural networks alpine skiing; GNSS measurements; Inertial Measurement Unit (IMU) measurements; statistical models; LWPR; neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Nemec, B.; Petrič, T.; Babič, J.; Supej, M. Estimation of Alpine Skier Posture Using Machine Learning Techniques. Sensors 2014, 14, 18898-18914.

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