Multiple Density Maps Information Fusion for Effectively Assessing Intensity Pattern of Lifelogging Physical Activity
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1016
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2016.06.073
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 199
- Volume
- 220
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Access exception
- Month of publication
- August
- 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
-
3
- Research group(s)
-
-
- Citation count
- 32
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper explores the use of mobile phones and fitness tracking devices for identifying patterns of physical activity automatically. A novel ellipse fitting model was applied to remove irregularity in the data produced by these inexpensive sensors to give high reliability in determining the activity level of the wearer, unobtrusively and accurately. This work has since gone on to be cited by researchers in a number of diverse fields including health, engineering, informatics, virtual reality, mobile computing and the Internet of Things.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
