4.7 Article

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

Journal

IEEE SENSORS JOURNAL
Volume 14, Issue 4, Pages -

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2013.2292585

Keywords

Bayesian learning; coordination; energy expenditure; velocity; wearable sensor

Funding

  1. Swiss National Science Foundation [320030-127554]
  2. Inter-Institutional Centre of Translational Biomechanics
  3. Swiss National Science Foundation (SNF) [320030_127554] Funding Source: Swiss National Science Foundation (SNF)

Ask authors/readers for more resources

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, 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 error of 0.9 +/- 11.3%.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available