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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239908/ https://www.ncbi.nlm.nih.gov/pubmed/25313492 http://dx.doi.org/10.3390/s141018898 |
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author | Nemec, Bojan Petrič, Tadej Babič, Jan Supej, Matej |
author_facet | Nemec, Bojan Petrič, Tadej Babič, Jan Supej, Matej |
author_sort | Nemec, Bojan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4239908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-42399082014-11-21 Estimation of Alpine Skier Posture Using Machine Learning Techniques Nemec, Bojan Petrič, Tadej Babič, Jan Supej, Matej Sensors (Basel) Article 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. MDPI 2014-10-13 /pmc/articles/PMC4239908/ /pubmed/25313492 http://dx.doi.org/10.3390/s141018898 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nemec, Bojan Petrič, Tadej Babič, Jan Supej, Matej Estimation of Alpine Skier Posture Using Machine Learning Techniques |
title | Estimation of Alpine Skier Posture Using Machine Learning Techniques |
title_full | Estimation of Alpine Skier Posture Using Machine Learning Techniques |
title_fullStr | Estimation of Alpine Skier Posture Using Machine Learning Techniques |
title_full_unstemmed | Estimation of Alpine Skier Posture Using Machine Learning Techniques |
title_short | Estimation of Alpine Skier Posture Using Machine Learning Techniques |
title_sort | estimation of alpine skier posture using machine learning techniques |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239908/ https://www.ncbi.nlm.nih.gov/pubmed/25313492 http://dx.doi.org/10.3390/s141018898 |
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