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INNOVATIVE METHODOLOGIES

An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer

¹Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts; ; ²Department of Exercise Science, Lyndon State College, Lyndonville, Vermont; ; ³Department of Exercise and Health Sciences, University of Massachusetts, Boston, Massachusetts; ; ⁴Department of Exercise, Sport and Leisure Studies, University of Tennessee, Knoxville, Tennessee; and ; ⁵Department of Kinesiology, University of Massachusetts, Amherst, Massachusetts

Submitted 4 May 2009 ; accepted in final form 23 July 2009

The aim of this investigation was to develop and test two artificial neural networks (ANN) to apply to physical activity data collected with a commonly used uniaxial accelerometer. The first ANN model estimated physical activity metabolic equivalents (METs), and the second ANN identified activity type. Subjects (n = 24 men and 24 women, mean age = 35 yr) completed a menu of activities that included sedentary, light, moderate, and vigorous intensities, and each activity was performed for 10 min. There were three different activity menus, and 20 participants completed each menu. Oxygen consumption (in ml·kg^–1·min^–1) was measured continuously, and the average of minutes 4–9 was used to represent the oxygen cost of each activity. To calculate METs, activity oxygen consumption was divided by 3.5 ml·kg^–1·min^–1 (1 MET). Accelerometer data were collected second by second using the Actigraph model 7164. For the analysis, we used the distribution of counts (10th, 25th, 50th, 75th, and 90th percentiles of a minute's second-by-second counts) and temporal dynamics of counts (lag, one autocorrelation) as the accelerometer feature inputs to the ANN. To examine model performance, we used the leave-one-out cross-validation technique. The ANN prediction of METs root-mean-squared error was 1.22 METs (confidence interval: 1.14–1.30). For the prediction of activity type, the ANN correctly classified activity type 88.8% of the time (confidence interval: 86.4–91.2%). Activity types were low-level activities, locomotion, vigorous sports, and household activities/other activities. This novel approach of applying ANNs for processing Actigraph accelerometer data is promising and shows that we can successfully estimate activity METs and identify activity type using ANN analytic procedures.

signal processing