Detecting abnormal events on binary sensors in smart home environments
- Submitting institution
-
University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 251740274
- Type
- D - Journal article
- DOI
-
10.1016/j.pmcj.2016.06.012
- Title of journal
- Pervasive and Mobile Computing
- Article number
- -
- First page
- 32
- Volume
- 33
- Issue
- -
- ISSN
- 1574-1192
- Open access status
- Compliant
- Month of publication
- June
- 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
- No
- Number of additional authors
-
2
- Research group(s)
-
C - Health Informatics
- Citation count
- 16
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Abnormal sensor events caused by noise or chance occurrences can be shown to damage the behaviour of classifiers, which is worrying when trying to classify potentially life-threatening events. This work automatically identifies and cleans-up common abnormalities, both random and systematic. It led to a volume of further work on sensor data quality evaluation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
