Towards zero re-training for long-term hand gesture recognition via ultrasound sensing
- Submitting institution
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University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14570016
- Type
- D - Journal article
- DOI
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10.1109/JBHI.2018.2867539
- Title of journal
- IEEE Journal of Biomedical and Health Informatics
- Article number
- 0
- First page
- 1639
- Volume
- 23
- Issue
- 4
- ISSN
- 2168-2194
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- 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)
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B - Computational Intelligence
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- One of the first articles on long-term hand gesture recognition with zero retraining. Has been selected as a feature paper for hand gesture recognition by the IEEE Journal of Biomedical and Health Informatics. Potential clinical applications include prosthetic hand control, treatment of neuropathic pain, and stroke rehabilitation. The algorithm has been adapted for hand prosthetics such as iLimb by Touch Bionics Ltd (Hugh Gill, Technical Director, hugh.gill@touchbionics.com). This work also leads international research in practical prosthetics, with experiments being carried out in Huashan Hospital in Shanghai, China.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
