Ellipse fitting model for improving the effectiveness of life‐logging physical activity measures in an Internet of Things environment. Issue 5 (1st September 2016)

• Record Type: Journal Article
• Authors: Qi, Jun; Yang, Po; Hanneghan, Martin; Fan, Dina; Deng, Zhikun; Dong, Feng
• Is Part Of: IET networks. Volume 5:Issue 5(2016)
• Page Start: 107
• Page End: 113
• Publication Date: 2016-09-01
• Subjects: Internet of Things -- health care -- medical computing; ellipse fitting model -- life‐logging physical activity measure -- Internet of Things environment -- IoT environment -- interdisciplinary health care research -- health care collaboration -- validity improvement method -- health care platform -- PA measure; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2015.0109
• Languages: English
• ISSNs: 2047-4954

Abstract

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.