ITE Transactions on Media Technology and Applications
Online ISSN : 2186-7364
ISSN-L : 2186-7364
Volume 6, Issue 4
Displaying 1-8 of 8 articles from this issue
Special Section on IDW '17
Regular Section
  • Chengju Zhou, Ikuhisa Mitsugami, Fumio Okura, Kota Aoki, Yasushi Yagi
    2018 Volume 6 Issue 4 Pages 286-296
    Published: 2018
    Released on J-STAGE: October 01, 2018

    Conventional approaches to the assessment of growth among children involve manual evaluation and treat different aspects of growth status separately. In contrast, this study presents an automated method for assessing growth status that considers various aspects of growth simultaneously. We first applied the dual-task paradigm (where two tasks are performed simultaneously) to collect data on anthropometric, kinematic, and cognitive aspects of growth at the same time. With the collected data on a large number of typically developing individuals, we constructed a statistical model of growth features and ages and also estimated participants' ages using regression analysis. By comparing the value for a participant to the average level of performance, we were able to provide an initial judgment of a child's growth status. The experiment results demonstrated that, among children, the growth features developed with age and that the estimation of growth status using this model was feasible.

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