DocumentCode
110964
Title
Corrections to “Predicting Free-Living Energy Expenditure Using a Miniaturized Ear-Worn Sensor: An Evaluation Against Doubly Labeled Water” [Feb 14 566-575]
Author
Bouarfa, Loubna ; Atallah, Louis ; Kwasnicki, Richard Mark ; Pettitt, Claire ; Frost, Gordon ; Yang, Guo-Min
Author_Institution
Hamlyn Centre, Imperial College London, London, U.K.
Volume
61
Issue
11
fYear
2014
fDate
Nov. 2014
Firstpage
2818
Lastpage
2818
Abstract
In the above paper (ibid., vol. 61, no. 2, pp. 566-575, Feb. 2014), the mean absolute deviation was incorrect in the Abstract. This should have read as follows. Accurate estimation of daily total energy expenditure (EE) is a prerequisite for assisted weight management and assessing certain health conditions. The use of wearable sensors for predicting free-living EE is challenged by consistent sensor placement, user compliance, and estimation methods used. This paper examines whether a single ear-worn accelerometer can be used for EE estimation under free-living conditions. An EE prediction model as first derived and validated in a controlled setting using healthy subjects involving different physical activities. Ten different activities were assessed showing a tenfold cross validation error of 0.24. Furthermore, the EE prediction model shows a mean absolute deviation (MAD) below 1.2 metabolic equivalent of tasks. The same model was applied to a free-living setting with a different population for further validation. The results were compared against those derived from doubly labeled water. In free-living settings, the predicted daily EE has a correlation of 0.74, p 0.008, and a MAD of 272 kcal day. These results demonstrate that laboratory-derived prediction models can be used to predict EE under free-living conditions.
Keywords
Accelerometers; Biomedical measurement; Feature extraction; Wearable sensors;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/TBME.2014.2359079
Filename
6924845
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