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  4. Estimation of Front-Crawl Energy Expenditure Using Wearable Inertial Measurement Units
 
research article

Estimation of Front-Crawl Energy Expenditure Using Wearable Inertial Measurement Units

Dadashi, Farzin  
•
Millet, Gregoire
•
Aminian, Kamiar  
2014
IEEE Sensors Journal

Measurement of the energy expenditure is crucial to understand the biophysics of any kind of human locomotion. Despite the promising application of inertial measurement unit (IMU) for quantification of the energy expenditure during human on-land activities, it has never been deployed before to calculate the aquatic activities energy expenditure. Wearable IMUs were used in our study to capture biomechanically interpretable descriptors of swimming. These descriptors were fed as the input to a Bayesian linear model for estimation of the energy expenditure. To enhance generalization capacity of the estimation, a non-linear adjustment of the Bayesian model was devised using swimmer’s anthropometric parameters. We used a set of four waterproofed IMUs worn on forearms, sacrum and right shank of eighteen swimmers to extract the main spatio-temporal determinants of the front-crawl energy expenditure. The swimmers performed three 300-m trials at 70%, 80% and 90% of their 400-m personal best time. At the end of each 300-m the reference value of energy expenditure was measured based on indirect calorimetry and blood lactate concentration. The assessment of the proposed model on the test data shows a strong association between the estimated and reference energy expenditure (Spearman’s rho = 0.93, p-value <0.001) and a high relative precision of 9.4%. The backward elimination of model parameters with minimum RMS error criterion shows that by excluding the features extracted from forearm sensors i.e. using only two IMUs we can still achieve an accuracy of 0.9±11.3%.

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Type
research article
DOI
10.1109/Jsen.2013.2292585
Web of Science ID

WOS:000331954200003

Author(s)
Dadashi, Farzin  
Millet, Gregoire
Aminian, Kamiar  
Date Issued

2014

Publisher

Institute of Electrical and Electronics Engineers

Published in
IEEE Sensors Journal
Volume

14

Issue

4

Start page

1020

End page

1027

Subjects

Bayesian learning

•

Coordination

•

Energy expenditure.

•

Velocity

•

Wearable sensor

URL

URL

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6675007
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LMAM  
Available on Infoscience
November 19, 2013
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/97103
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