SmartWall: Novel RFID-Enabled Ambient Human Activity Recognition Using Machine Learning for Unobtrusive Health Monitoring
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 58
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2019.2917125
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 68022
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
https://ieeexplore.ieee.org/document/8716524
- 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
- No
- Number of additional authors
-
7
- Research group(s)
-
-
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is part funded by the EU H2020 MSCA ITN SECRET project (722424). It explored an innovative touchless RFID-enabled smart-wall for assisted living within a home or community setting, offering people the prospect of independent living assisted by technology. Through data collected by the RFID sensors, human activities are recognised using a multivariate Gaussian framework. Contributions of this work is particularly useful for remote monitoring of the elderly. This work contributed to the success of EU H2020 eBorder project funded under the MSCA RISE programme (872878) and currently being explored for its suitability to border control in the project.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
