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

2.50
Hdl Handle:
http://hdl.handle.net/10547/623788
Title:
Ellipse fitting model for improving the effectiveness of life-logging physical activity measures in an Internet of Things environment
Authors:
Qi, Jun; Yang, Po ( 0000-0002-8553-7127 ) ; Hanneghan, Martin; Fan, Dina; Deng, Zhikun ( 0000-0002-3659-757X ) ; Dong, Feng ( 0000-0003-4122-8012 )
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.
Citation:
Qi J, Yang P, Hanneghan M, Fan D, Deng Z, Dong F (2016) 'Ellipse fitting model for improving the effectiveness of life-logging physical activity measures in an Internet of Things environment', IET Networks, 5 (5), pp.107-113.
Publisher:
Institution of Engineering and Technology
Journal:
IET Networks
Issue Date:
13-Oct-2016
URI:
http://hdl.handle.net/10547/623788
DOI:
10.1049/iet-net.2015.0109
Additional Links:
https://ieeexplore.ieee.org/document/7587513
Type:
Article
Language:
en
ISSN:
2047-4954
EISSN:
2047-4962
Appears in Collections:
Computing

Full metadata record

DC FieldValue Language
dc.contributor.authorQi, Junen
dc.contributor.authorYang, Poen
dc.contributor.authorHanneghan, Martinen
dc.contributor.authorFan, Dinaen
dc.contributor.authorDeng, Zhikunen
dc.contributor.authorDong, Fengen
dc.date.accessioned2020-01-14T11:25:09Z-
dc.date.available2020-01-14T11:25:09Z-
dc.date.issued2016-10-13-
dc.identifier.citationQi J, Yang P, Hanneghan M, Fan D, Deng Z, Dong F (2016) 'Ellipse fitting model for improving the effectiveness of life-logging physical activity measures in an Internet of Things environment', IET Networks, 5 (5), pp.107-113.en
dc.identifier.issn2047-4954-
dc.identifier.doi10.1049/iet-net.2015.0109-
dc.identifier.urihttp://hdl.handle.net/10547/623788-
dc.description.abstractThe 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.en
dc.language.isoenen
dc.publisherInstitution of Engineering and Technologyen
dc.relation.urlhttps://ieeexplore.ieee.org/document/7587513en
dc.subjectInternet of Thingsen
dc.subjectwearable computingen
dc.subjectwearable monitoringen
dc.subjectlife-logging dataen
dc.subjectphysical activityen
dc.subjectB800 Medical Technologyen
dc.titleEllipse fitting model for improving the effectiveness of life-logging physical activity measures in an Internet of Things environmenten
dc.typeArticleen
dc.identifier.eissn2047-4962-
dc.identifier.journalIET Networksen
dc.date.updated2020-01-14T11:22:56Z-
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