Artificial Neural Networks to Predict Activity Type and Energy Expenditure in Youth
- 1 September 2012
- journal article
- Published by Ovid Technologies (Wolters Kluwer Health) in Medicine & Science in Sports & Exercise
- Vol. 44 (9), 1801-1809
- https://doi.org/10.1249/mss.0b013e318258ac11
Abstract
Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous–intensity games or sports. During each trial, participants wore an ActiGraph GT1M on the right hip, and V˙O2 was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square error (RMSE). Results As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30–40% lower than the conventional regression-based approaches. Conclusions ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.Keywords
This publication has 18 references indexed in Scilit:
- Calibration and Validation of Wearable MonitorsMedicine & Science in Sports & Exercise, 2012
- Statistical Considerations in the Analysis of Accelerometry-Based Activity Monitor DataMedicine & Science in Sports & Exercise, 2012
- Identification of Children's Activity Type with Accelerometer-Based Neural NetworksMedicine & Science in Sports & Exercise, 2011
- Comparison of Accelerometer Cut Points for Predicting Activity Intensity in YouthMedicine & Science in Sports & Exercise, 2011
- Evaluation of Neural Networks to Identify Types of Activity Using AccelerometersMedicine & Science in Sports & Exercise, 2011
- An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometerJournal of Applied Physiology, 2009
- State of the Art Reviews: Measurement of Physical Activity in Children and AdolescentsAmerican Journal of Lifestyle Medicine, 2007
- Improving physical activity assessment in prepubertal children with high-frequency accelerometry monitoring: A methodological issuePreventive Medicine, 2007
- Development of Novel Techniques to Classify Physical Activity Mode Using AccelerometersMedicine & Science in Sports & Exercise, 2006
- A novel method for using accelerometer data to predict energy expenditureJournal of Applied Physiology, 2006