An artificial neural network model using raw acceleration signals significantly reduced errors in estimating energy expenditure compared to IDEEA (P<0.01) and ActiGraph (P<0.001) monitors.
Observational (n=102)
Does an artificial neural network model using raw acceleration signals improve the estimation of energy expenditure compared to standard accelerometers in healthy adults?
An artificial neural network using raw acceleration signals provides more accurate estimates of energy expenditure than traditional linear regression models or multi-sensor arrays.
p-value: p=<0.01
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 et al. (Fri,) conducted a observational in Healthy adults (n=102). Artificial neural network (ANN) model using raw acceleration signals vs. IDEEA monitor and ActiGraph monitor was evaluated on Mean absolute errors in estimating energy expenditure (p=<0.01). An artificial neural network model using raw acceleration signals significantly reduced errors in estimating energy expenditure compared to IDEEA (P<0.01) and ActiGraph (P<0.001) monitors.