Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device
- Submitting institution
-
Glasgow Caledonian University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 33621794
- Type
- D - Journal article
- DOI
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10.1016/j.bspc.2016.10.016
- Title of journal
- Biomedical Signal Processing and Control
- Article number
- -
- First page
- 96
- Volume
- 33
- Issue
- -
- ISSN
- 1746-8094
- Open access status
- Compliant
- Month of publication
- December
- 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
-
4
- Research group(s)
-
-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Key paper contributions involved acceleration signal features sparse matrix sampling within an embedded device and matching pursuit compressive sensing. Comparable fall detection systems increase complexity and power requirements, while this application breaks the trend to achieve the same complexity with power efficiency and improving wearable device battery life through reducing data transmission by over 70%. This work continues cross institute research with GCU, UWS and Qatar universities on falls with 5 papers over 2 years in the area. The author was invited to Qatar University to present key-findings and continue collaboration links.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -