Open AccessArtificial Neural Networks to Predict Activity Type and Energy Expenditure in Youth
Author(s) -
Stewart G. Trost,
Weng Keen Wong,
Karen A. Pfeiffer,
Yujie Zheng
Publication year - 2012
Publication title -
medicine and science in sports and exercise
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.703
H-Index - 224
eISSN - 1530-0315
pISSN - 0195-9131
DOI - 10.1249/mss.0b013e318258ac11
Subject(s) - mean squared error , accelerometer , energy expenditure , percentile , waist , artificial neural network , physical activity , statistics , cardiorespiratory fitness , mathematics , physical therapy , medicine , computer science , artificial intelligence , body mass index , pathology , endocrinology , operating system
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.