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J Appl Physiol (July 19, 2007). doi:10.1152/japplphysiol.00429.2007
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Submitted on April 19, 2007
Accepted on July 16, 2007

An Artificial Neural Network Model of Energy Expenditure using Non-integrated Acceleration Signals

Megan P. Rothney1*, Megan Neumann2, Ashley Beziat2, and Kong Y. Chen3

1 Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
2 Medicine\Gastroenterology, Vanderbilt University, Nashville, Tennessee, United States
3 NIDDK\CEB, NIH, Bethesda, Maryland, United States

* To whom correspondence should be addressed. E-mail: megan.p.rothney{at}vanderbilt.edy.

Accelerometers are a promising tool for characterizing physical activity (PA) patterns in free-living. The major limitation in their widespread use to date has been a lack of precision in estimating energy expenditure (EE), which may be attributed to the over-simplified time-integrated acceleration signals and subsequent use of linear regression models for EE estimation. In this study, we collected bi-axial raw (32 Hz) acceleration signals at the hip to develop a relationship between acceleration and minute-to-minute EE in 102 healthy adults using EE data collected for nearly 24 hours in a room calorimeter as the reference standard. From each minute of acceleration data, we extracted 10 signal characteristics (features) which we felt had the potential to characterize EE intensity. Using these data we developed a feed-forward/back-propagation artificial neural network (ANN) model with one hidden layer (12x20x1 nodes). Results of the ANN were compared to estimations using the ActiGraph monitor, a uni-axial accelerometer, and the IDEEA monitor, an array of five accelerometers. After training and validation (leave-one-subject out), the ANN showed significantly reduced mean absolute errors (0.29±0.10 kcal/min), mean squared errors (0.23±0.14 kcal2/min2), and difference in total EE (21±115 kcal/day), when compared to both the IDEEA (p < 0.01) and a regression model for the ActiGraph accelerometer (p < 0.001). Thus, ANN combined with raw acceleration signals is a promising approach to link body accelerations to EE. Further validation is needed to understand the performance of the model for different PA types under free-living conditions.




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