Fall detection and human activity classification using wearable sensors and compressed sensing
- Submitting institution
-
University of the West of Scotland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22685073
- Type
- D - Journal article
- DOI
-
10.1007/s12652-019-01214-4
- Title of journal
- Journal of Ambient Intelligence and Humanized Computing
- Article number
- -
- First page
- 349
- Volume
- 11
- Issue
- 1
- ISSN
- 1868-5137
- Open access status
- Compliant
- Month of publication
- January
- 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
-
4
- Research group(s)
-
-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The proposed system collects real-time data from the accelerometer and the gyroscope sensors, and exploits the benefit in enhancing the performance of the system by compressive sensing (CS) capabilities that increases battery life. To the best of the our’ knowledge, the only other work on CS-based architecture taking advantage of more than a wearable sensor, is the one that combines the collected data from the camera and the accelerometer. The proposed system demonstration helped us to secure two projects namely Innovate UK Grant #11991 with Visual Management Systems Limited and EU project SAFE Rural Health grant #619483-EPP-1-2020-1-UK-EPPKA2-CBHE-JP.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -