DocumentCode :
110146
Title :
Estimation of Front-Crawl Energy Expenditure Using Wearable Inertial Measurement Units
Author :
Dadashi, Farzin ; Millet, Gregoire P. ; Aminian, Kamiar
Author_Institution :
Lab. of Movement Anal. & Meas., Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland
Volume :
14
Issue :
4
fYear :
2014
fDate :
Apr-14
Firstpage :
1020
Lastpage :
1027
Abstract :
Energy expenditure measurement 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 this paper to capture biomechanically interpretable descriptors of swimming. These descriptors were fed as inputs to a Bayesian linear model for estimation of the energy expenditure. To enhance generalization capacity of the estimator, 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 spatiotemporal 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, ) 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 error of 0.9±11.3%.
Keywords :
gait analysis; haemodynamics; inertial systems; portable instruments; units (measurement); Bayesian linear model; anthropometric parameters; biomechanically interpretable descriptors; blood lactate concentration; distance 300 m to 400 m; forearms; front-crawl energy expenditure; indirect calorimetry; right shank; sacrum; spatiotemporal determinants; swimming; wearable inertial measurement units; Bayes methods; Biological system modeling; Blood; Data models; Estimation; Feature extraction; Sensors; Bayesian learning; coordination; energy expenditure; velocity; wearable sensor;
fLanguage :
English
Journal_Title :
Sensors Journal, IEEE
Publisher :
ieee
ISSN :
1530-437X
Type :
jour
DOI :
10.1109/JSEN.2013.2292585
Filename :
6675007
Link To Document :
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