Detecting Physical Activity within Lifelogs towards Preventing Obesity and Aiding Ambient Assisted Living
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 958
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2016.02.088
- Title of journal
- NEUROCOMPUTING
- Article number
- -
- First page
- 110
- Volume
- 230
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- 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
-
2
- Research group(s)
-
-
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper evaluates the performance of supervised machine learning in distinguishing physical activity. It is an outcome of an international collaboration between colleagues at 1) the Department Computer Science at Dartmouth College (USA), which is one of the top computer science schools in the USA, and 2) the Faculty of Computer Science at University of Vienna (Austria), which is the principal centre for teaching and research in computer science and business informatics in Austria. The proposed work has been cited in other areas like smoking cessation intervention and smart lighting control.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
