The classification of minor gait alterations using wearable sensors and deep learning
- Submitting institution
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The University of Hull
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1400138
- Type
- D - Journal article
- DOI
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10.1109/TBME.2019.2900863
- Title of journal
- IEEE transactions on bio-medical engineering / Bio-medical Engineering Group
- Article number
- -
- First page
- 3136
- Volume
- 66
- Issue
- 11
- ISSN
- 0018-9294
- Open access status
- Exception within 3 months of publication
- Month of publication
- February
- 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
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1
- Research group(s)
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-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Gait abnormality in human can significantly affect the quality of life and currently is diagnosed by specialist clinicians using a combination of previous diagnoses, gait function observation, genetic data, MRI, CT and overall health. The work proposed in this paper is very significant as it evaluates the use of non-invasive wearable sensors with deep learning to classify gait without involvement of gait analyst. As demonstrated by experimental results, we believe the research will have substantial impact in 2) automating gait diagnosis (for quick and accurate diagnosis) and 2) remotely diagnosing gait patients.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -