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 ϭ 24 men and 24 women, mean age ϭ 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 ⅐ kg Ϫ1 ⅐ min Ϫ1 ) 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 ⅐ kg Ϫ1 ⅐ min Ϫ1 (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-bysecond 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 OBJECTIVE MEASUREMENT OF PHYSICAL activity (PA) in a freeliving setting is essential for understanding the determinants of individuals' PA behavior, evaluating the effectiveness of interventions designed to increase PA, performing PA surveillance, and quantifying the relationship between PA dose and health outcomes. Commensurate with the widespread interest in objective PA assessment, there are many wearable devices available for the assessment of free-living PA. Accelerometer sensors, in particular, have been selected as the device of choice; however, data processing methods of accelerometer output have yet to realize their promise to provide accurate estimates of PA type, patterns, and energy expenditure. Nearly all applications of accelerometer-based PA monitors to quantify PA have used a similar approach to process and interpret accelerometer data by collecting simultaneous recordings of energy expenditure using direct or indirect calorimetry and accelerometer output. The relationship between the monitor output and energy expenditure is modeled using linear regression techniques to develop a formula either to estimate energy expended in PA (PAEE) or to establish a set of "cut-points" used to determine time spent in different intensity ranges (6, 7, 9, 10, 12, 20, 22) . While these approaches have been well received by the scientific community and often result in relatively small or nonsignificant mean differences between estimated and actual PAEE when applied to a large group of subjects, the individual estimation errors are often substantial (2, 16). The lack of satisfactory results in the comparison of estimated PAEE from PA monitors to actual PAEE is due to the regression not fitting all modes of activity. The regression equations developed from locomotion activities provide poor estimates of energy expenditure for lifestyle activities that involve movement that is not captured by the accelerometer (e.g., upper body movements), and equations developed on lifestyle activities are not valid when applied to locomotion behaviors (1, 21). In an effort to address this concern, variations of the linear regression approach by employing different equations for different types of activity have been proposed (6, 9, 12) . Alternatively, use of two sensors simultaneously, such as heart rate and accelerometers and processing data with branched chain equation modeling (3) and cross-sectional time series (24), have also been used in an attempt to improve PAEE estimates. Another common limitation of translating accelerometer data to physiologically meaningful energy expenditure metrics is that the single integrated accelerometer signal averaged over time essentially eliminates the rich features of the accelerometer signal and contributes to the imprecision of the PAEE estimates (17). Another approach to improve estimates of PAEE is to use pattern recognition or "machine learning" approaches to the processing of data from accelerometers. Our group reported success in applying hidden Markov models (HMM; a type of probabilistic pattern recognition algorithm for times series data) and other statistical classification tools to accelerometer data to identify specific modes of activities (17). Other groups have also had success identifying specific activities by applying different types of pattern recognition algorithms to accelerometer data (5, 14, 15, 21 ). This may ultimately lead to
REF , in the first step to predict the physical activity type and then afterwards separately to predict the MET values.
An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer
Journal of applied physiology
Journal of applied physiology
Medicine Chemistry