Ellipse fitting model for improving the effectiveness of life‐logging physical activity measures in an Internet of Things environment
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 co...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-09-01
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| Series: | IET Networks |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-net.2015.0109 |
| _version_ | 1818738299877982208 |
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| author | Jun Qi Po Yang Martin Hanneghan Dina Fan Zhikun Deng Feng Dong |
| author_facet | Jun Qi Po Yang Martin Hanneghan Dina Fan Zhikun Deng Feng Dong |
| author_sort | Jun Qi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-18T01:06:44Z |
| format | Article |
| id | doaj.art-89f5c36f71cb47af9962deb2905a932f |
| institution | Directory Open Access Journal |
| issn | 2047-4954 2047-4962 |
| language | English |
| last_indexed | 2024-12-18T01:06:44Z |
| publishDate | 2016-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Networks |
| spelling | doaj.art-89f5c36f71cb47af9962deb2905a932f2022-12-21T21:26:13ZengWileyIET Networks2047-49542047-49622016-09-015510711310.1049/iet-net.2015.0109Ellipse fitting model for improving the effectiveness of life‐logging physical activity measures in an Internet of Things environmentJun Qi0Po Yang1Martin Hanneghan2Dina Fan3Zhikun Deng4Feng Dong5Department of Computer ScienceLiverpool John Moores UniversityLiverpoolUKDepartment of Computer ScienceLiverpool John Moores UniversityLiverpoolUKDepartment of Computer ScienceLiverpool John Moores UniversityLiverpoolUKCentre of Computer Graphics and VisualizationBedfordshire UniversityLutonUKCentre of Computer Graphics and VisualizationBedfordshire UniversityLutonUKCentre of Computer Graphics and VisualizationBedfordshire UniversityLutonUKThe 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.https://doi.org/10.1049/iet-net.2015.0109ellipse fitting modellife‐logging physical activity measureInternet of Things environmentIoT environmentinterdisciplinary health care researchhealth care collaboration |
| spellingShingle | Jun Qi Po Yang Martin Hanneghan Dina Fan Zhikun Deng Feng Dong Ellipse fitting model for improving the effectiveness of life‐logging physical activity measures in an Internet of Things environment IET Networks ellipse fitting model life‐logging physical activity measure Internet of Things environment IoT environment interdisciplinary health care research health care collaboration |
| title | Ellipse fitting model for improving the effectiveness of life‐logging physical activity measures in an Internet of Things environment |
| title_full | Ellipse fitting model for improving the effectiveness of life‐logging physical activity measures in an Internet of Things environment |
| title_fullStr | Ellipse fitting model for improving the effectiveness of life‐logging physical activity measures in an Internet of Things environment |
| title_full_unstemmed | Ellipse fitting model for improving the effectiveness of life‐logging physical activity measures in an Internet of Things environment |
| title_short | Ellipse fitting model for improving the effectiveness of life‐logging physical activity measures in an Internet of Things environment |
| title_sort | ellipse fitting model for improving the effectiveness of life logging physical activity measures in an internet of things environment |
| topic | ellipse fitting model life‐logging physical activity measure Internet of Things environment IoT environment interdisciplinary health care research health care collaboration |
| url | https://doi.org/10.1049/iet-net.2015.0109 |
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