A consensus novelty detection ensemble approach for anomaly detection in activities of daily living
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1 - 1072366
- Type
- D - Journal article
- DOI
-
10.1016/j.asoc.2019.105613
- Title of journal
- Applied Soft Computing
- Article number
- S156849461930393X
- First page
- -
- Volume
- 83
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Access exception
- Month of publication
- July
- Year of publication
- 2019
- 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
- No
- Number of additional authors
-
2
- Research group(s)
-
A - Computing and Informatics Research Centre
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper reports our proposed innovative methodologies and research results for abnormality detection in activities of daily living. The significance of this paper is that it led us to collaborate with Alcuris Ltd. (www.alcuris.co.uk) to investigate integration of our solution into their Connected Care hardware system. The company has already developed the hardware and communication system and an intelligent solution will enhance the performance of their system in support of independent living. A collaborative proposal through Healthy Ageing Industrial Strategy was submitted to Innovate UK and it was unsuccessful. A KTP application with the company is in preparation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
