Artificial neural networks to predict activity type and energy expenditure in youth
Trost, Stewart G., Wong, Weng-Keen, Pfeiffer, Karen, & Zheng, Yonglei (2012) Artificial neural networks to predict activity type and energy expenditure in youth. Medicine and Science in Sports and Exercise, 44(9), pp. 1801-1809.
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 GTIM on the right hip, and (V) Over dotO(2) 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 en-or (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.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Journal Article|
|Additional Information:||ISI Document Delivery No.: 990IQ
Times Cited: 8
Cited Reference Count: 19
Trost, Stewart G. Wong, Weng-Keen Pfeiffer, Karen A. Zheng, Yonglei
Eunice Kennedy Shriver National Institute of Child Health and Human Development [NICHD R01 55400]
This study was supported by funding by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD R01 55400).
Lippincott williams & wilkins
Philadelphia; <Go to ISI>://WOS:000307624600022
|Keywords:||objective assessment, validity, children, adolescents, pattern, recognition, physical-activity, accelerometer, calibration|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health|
|Deposited On:||22 May 2014 01:40|
|Last Modified:||22 May 2014 01:40|
Repository Staff Only: item control page