Lifelogging Data Validation Model for Internet of Things enabled Personalized Healthcare
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1410
- Type
- D - Journal article
- DOI
-
10.1109/TSMC.2016.2586075
- Title of journal
- IEEE transactions on systems, man, and cybernetics. Systems
- Article number
- -
- First page
- 50
- Volume
- 48
- Issue
- 1
- ISSN
- 2168-2216
- Open access status
- Access exception
- Month of publication
- July
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
8
- Research group(s)
-
-
- Citation count
- 66
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is an output of an international collaboration among researchers at five universities in the UK, Lithuania and USA, supported by four research grants such as EP/L023830/1. It addresses lifelogging personal data validation and analysis that are essential to healthcare studies. The work proposes a novel model for lifelogging physical activity data validation, which eliminates irregular uncertainties and estimates data reliability in IoT-based healthcare systems. A case study was conducted to confirm the model’s performance and effectiveness. The paper has attracted considerable interest from other researchers in fields like sensor networks and human-robot interaction (https://ieeexplore.ieee.org/document/7516690/citations?tabFilter=papers#citations).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
