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Ellipse fitting model for improving the effectiveness of life-logging physical activity measures in an Internet of Things environment

  • Jun Qi
    ,
  • Po Yang
    ,
  • Martin Hanneghan
    ,
  • Dina Fan
    ,
  • Zhikun Deng
    ,
  • Feng Dong
Research Output: Contribution to journal Article Peer-review

Abstract

The popular use of wearable devices and mobile phones makes the effective capture of life-logging physical activity (PA) data in an Internet of Things (IoT) environment possible. The effective collection of measures of PA in the long term is beneficial to interdisciplinary healthcare research and collaboration from clinicians, researchers and patients. However, due to heterogeneity of connected devices and rapid change of diverse life patterns in an IoT environment, life-logging PA information captured by mobile devices usually contains much uncertainty. In this study, the authors project the distribution of irregular uncertainty by defining a walking speed related score named as daily activity in physical space and present an ellipse-fitting model-based validity improvement method for reducing uncertainties of life-logging PA measures in an IoT environment. The experimental results reflect that the proposed method remarkably improves the validity of PA measures in a healthcare platform.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 107-113

Journal (Volume, Issue Number)

IET Networks (Volume 5, Issue 5)

Publication milestones

  • Published - 13/10/2016

Publication status

Published - 13/10/2016

ISSN

2047-4954

External Publication IDs

  • handle.net: 10547/623788
  • Scopus: 84991199528

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