Ultrasound-based sensing models for finger motion classification
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14569798
- Type
- D - Journal article
- DOI
-
10.1109/JBHI.2017.2766249
- Title of journal
- IEEE Journal of Biomedical and Health Informatics
- Article number
- -
- First page
- 1395
- Volume
- 22
- Issue
- 5
- ISSN
- 2168-2194
- Open access status
- Not compliant
- Month of publication
- October
- Year of publication
- 2017
- 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
- Yes
- Number of additional authors
-
5
- Research group(s)
-
B - Computational Intelligence
- Citation count
- 27
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work set a new benchmark adopted by research into different sensing modality based motion recognition, including skin strain (Jiang et al., IEEE TIE 2020), barometric pressure (Shull et al., IEEE TNSRE 2019) and electromyography (Yang et al., IEEE THMS 2019). Its successful application has impacted further investigation into ultrasound based motion recognition of lower limbs (Jahanandish et al., IEEE JBHI 2019), wrists and fingers (He et al., IEEE TBME 2019) and hips, knees and ankles (Rabe et al., IEEE BioRob 2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -