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  4. Automatic Front-Crawl Temporal Phase Detection Using Adaptive Filtering of Inertial Signals
 
research article

Automatic Front-Crawl Temporal Phase Detection Using Adaptive Filtering of Inertial Signals

Dadashi, Farzin  
•
Crettenand, Florent  
•
Millet, Grégoire  
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2013
Journal of Sports Sciences

This study introduces a novel approach for automatic temporal phase detection and inter-arm coordination estimation in front-crawl swimming using inertial measurement units (IMUs). We examined the validity of our method by comparison against a video-based system. Three waterproofed IMUs (composed of 3D accelerometer, 3D gyroscope) were placed on both forearms and sacrum of the swimmer. We used two underwater video cameras in side and frontal views as our reference system. Two independent operators performed the video analysis. To test our methodology, seven well-trained swimmers performed three 300-m trials in a 50-m indoor pool. Each trial was in a different coordination mode quantified by the index of coordination. We detected different phases of the arm stroke by employing orientation estimation techniques and a new adaptive change detection algorithm on inertial signals. The difference of 0.2±3.9% between our estimation and video-based system in assessment of the index of coordination was comparable to experienced operators’ difference (1.1±3.6%). The 95% limits of agreement of the difference between the two systems in estimation of the temporal phases were always less than 7.9% of the cycle duration. The inertial system offers an automatic easy-to-use system with timely feedback for the study of swimming.

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Type
research article
DOI
10.1080/02640414.2013.778420
Web of Science ID

WOS:000320223200011

Author(s)
Dadashi, Farzin  
Crettenand, Florent  
Millet, Grégoire  
Seifert, Ludovic
Komar, John
Aminian, Kamiar  
Date Issued

2013

Publisher

Taylor & Francis

Published in
Journal of Sports Sciences
Volume

31

Issue

11

Start page

1251

End page

1260

Subjects

inertial sensors

•

coordination

•

front crawl

•

slope change detection

•

Kalman filtering

URL

URL

http://www.tandfonline.com/doi/abs/10.1080/02640414.2013.778420
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LMAM  
Available on Infoscience
February 18, 2013
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/88964
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