Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic
- Submitting institution
-
University of the West of Scotland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13058180
- Type
- D - Journal article
- DOI
-
10.1016/j.asoc.2015.10.062
- Title of journal
- Applied Soft Computing
- Article number
- -
- First page
- 94
- Volume
- 39
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2015
- 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
-
4
- Research group(s)
-
-
- Citation count
- 53
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article presents an accelerometer-based novel multiple classifier fall detection and diagnostic system. The system is capable of not only detecting fall, but it can also identify its type (intensity and direction). It achieves overall 99% in fall detection recall, precision and accuracy. The system is implemented on a wearable Shimmer device having direct application to real scenarios.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -