An artificial neural network model of energy expenditure using nonintegrated acceleration signals.
Accelerometers are a promising tool for characterizing physical activity 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 oversimplified time-integrated acceleration signals and subsequent use of linear regression models for EE estimation. In this study, we collected biaxial 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 h in a room calorimeter as the reference standard. From each 1 min of acceleration data, we extracted 10 signal characteristics (features) that 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 (12 x 20 x 1 nodes). Results of the ANN were compared with estimations using the ActiGraph monitor, a uniaxial accelerometer, and the IDEEA monitor, an array of five accelerometers. After training and validation (leave-one-subject out) were completed, the ANN showed significantly reduced mean absolute errors (0.29 +/- 0.10 kcal/min), mean squared errors (0.23 +/- 0.14 kcal(2)/min(2)), and difference in total EE (21 +/- 115 kcal/day), compared with 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 physical activity types under free-living conditions.
Rothney
Megan P
MP
Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, Tennessee, USA. rothneym@niddk.nih.gov
Neumann
Megan
M
Béziat
Ashley
A
Chen
Kong Y
KY
D000054:Acceleration; D000293:Adolescent; D000328:Adult; D000368:Aged; D002153:Calorimetry, Indirect / Q000379:methods; D004734:Energy Metabolism / Q000502:physiology*; D015444:Exercise / Q000502:physiology; D005260:Female; D006615:Hip; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods*; D009043:Motor Activity / Q000502:physiology*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results