title: Prediction models discriminating between nonlocomotive and locomotive activities in children using a triaxial accelerometer with a gravity-removal physical activity classification algorithm.
abstract: The aims of our study were to examine whether a gravity-removal physical activity classification algorithm (GRPACA) is applicable for discrimination between nonlocomotive and locomotive activities for various physical activities (PAs) of children and to prove that this approach improves the estimation accuracy of a prediction model for children using an accelerometer. Japanese children (42 boys and 26 girls) attending primary school were invited to participate in this study. We used a triaxial accelerometer with a sampling interval of 32 Hz and within a measurement range of ±6 G. Participants were asked to perform 6 nonlocomotive and 5 locomotive activities. We measured raw synthetic acceleration with the triaxial accelerometer and monitored oxygen consumption and carbon dioxide production during each activity with the Douglas bag method. In addition, the resting metabolic rate (RMR) was measured with the subject sitting on a chair to calculate metabolic equivalents (METs). When the ratio of unfiltered synthetic acceleration (USA) and filtered synthetic acceleration (FSA) was 1.12, the rate of correct discrimination between nonlocomotive and locomotive activities was excellent, at 99.1% on average. As a result, a strong linear relationship was found for both nonlocomotive (METs = 0.013×synthetic acceleration +1.220, R2 = 0.772) and locomotive (METs = 0.005×synthetic acceleration +0.944, R2 = 0.880) activities, except for climbing down and up. The mean differences between the values predicted by our model and measured METs were -0.50 to 0.23 for moderate to vigorous intensity (>3.5 METs) PAs like running, ball throwing and washing the floor, which were regarded as unpredictable PAs. In addition, the difference was within 0.25 METs for sedentary to mild moderate PAs (<3.5 METs). Our specific calibration model that discriminates between nonlocomotive and locomotive activities for children can be useful to evaluate the sedentary to vigorous PAs intensity of both nonlocomotive and locomotive activities.
authors.affiliation: Faculty of Engineering, Chiba Institute of Technology, Chiba, Japan.
authors.forename: Yuki
authors.initials: Y
authors.lastname: Hikihara
authors.affiliation: Division of Integrated Sciences, J. F. Oberlin University, Tokyo, Japan.
authors.forename: Chiaki
authors.initials: C
authors.lastname: Tanaka
authors.affiliation: Faculty of Service Industries, University of Marketing and Distribution Science, Hyogo, Japan.
authors.forename: Yoshitake
authors.initials: Y
authors.lastname: Oshima
authors.affiliation: Faculty of Informatics and Engineering, University of Electro-Communications, Tokyo Japan.
authors.forename: Kazunori
authors.initials: K
authors.lastname: Ohkawara
authors.affiliation: Department of Nutritional Education, National Institute of Health and Nutrition, Tokyo Japan.
authors.forename: Kazuko
authors.initials: K
authors.lastname: Ishikawa-Takata
authors.affiliation: Department of Nutritional Science, National Institute of Health and Nutrition, Tokyo Japan.
authors.forename: Shigeho
authors.initials: S
authors.lastname: Tanaka
mesh_terms: D000054:Acceleration; D061725:Accelerometry / Q000295:instrumentation*; D000367:Age Factors; D000465:Algorithms; D001481:Basal Metabolism; D001835:Body Weight; D002138:Calibration; D002648:Child; D004192:Discrimination, Psychological; D005260:Female; D006112:Gravitation; D006801:Humans; D008297:Male; D055686:Metabolic Equivalent; D008954:Models, Biological; D009043:Motor Activity / Q000502:physiology*; D011336:Probability; D012044:Regression Analysis; D015203:Reproducibility of Results; D013647:Task Performance and Analysis
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