A machine learning approach to measure and monitor physical activity in children
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 927
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2016.10.040
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 220
- Volume
- 228
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Compliant
- Month of publication
- November
- 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
-
10
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work results from an international collaboration between LJMU, Lionhead Studies (Microsoft), Edge Hill University, Swansea University and Deakin University, Australia, and outlines a way to measure physical activity (MVPA) and sedentary behaviour (SB) using machine learning. Subsequent research is ongoing with Prof. Stuart Fairclough from Edge Hill University and Prof. Gareth Stratton from Swansea University with students aged 10-15 years old from 11 schools in northwest England fitted with SenseWear Armband Mini (SWA) activity monitors. The algorithms from the paper are now being used in a UK-wide study to estimate MVPA and SB using the SWA devices.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
