WiFreeze: multiresolution scalograms for freezing of gait detection in Parkinson’s leveraging 5G spectrum with deep learning
- Submitting institution
-
Glasgow Caledonian University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 33621770
- Type
- D - Journal article
- DOI
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10.3390/electronics8121433
- Title of journal
- Electronics
- Article number
- 1433
- First page
- -
- Volume
- 8
- Issue
- 12
- ISSN
- 2079-9292
- Open access status
- Compliant
- Month of publication
- December
- 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
-
8
- Research group(s)
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-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a novel solution to detecting Parkinson’s disease Freezing of Gait through environmental WiFi channel state information. It was the first paper to utilize 5G WiFi standards within the field. Key contributions utilized very deep neural networks with signal processing to achieve high accuracy analysis metrics without requiring a wearable device. The work formed the basis of cross-institutional collaboration, including GCU, Manchester Metropolitan, University of Glasgow, Edinburgh Napier. Due to perceived quality and novelty, the article was selected for the journal volume cover, containing over 150 articles. The work led to 3 follow-up published journals since 2019.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -