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Estimation of alpine skier posture using machine learning techniques
ID
Nemec, Bojan
(
Author
),
ID
Petrič, Tadej
(
Author
),
ID
Babič, Jan
(
Author
),
ID
Supej, Matej
(
Author
)
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https://www.mdpi.com/1424-8220/14/10/18898
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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.
Language:
English
Keywords:
alpine skiing
,
GNSS measurements
,
Inertial Measurement Unit (IMU) 
measurements
,
statistical models
,
LWPR
,
neural networks
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FŠ - Faculty of Sport
Publication status:
Published
Publication version:
Version of Record
Year:
2014
Number of pages:
Str. 18898-18914
Numbering:
Vol. 14, iss. 10
PID:
20.500.12556/RUL-130622
UDC:
53
ISSN on article:
1424-8220
DOI:
10.3390/s141018898
COBISS.SI-ID:
28015143
Publication date in RUL:
16.09.2021
Views:
818
Downloads:
152
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Record is a part of a journal
Title:
Sensors
Shortened title:
Sensors
Publisher:
MDPI
ISSN:
1424-8220
COBISS.SI-ID:
10176278
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
13.10.2014
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