EchoFlex : Hand gesture recognition using ultrasound imaging
- Submitting institution
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The University of Bath
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 203022011
- Type
- E - Conference contribution
- DOI
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10.1145/3025453.3025807
- Title of conference / published proceedings
- CHI '17: Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems : Explore, Innovate, Inspire
- First page
- 1923
- Volume
- 2017-May
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- May
- Year of publication
- 2017
- URL
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-
- Supplementary information
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https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3025453.3025807&file=pn2761-file4.zip&download=true
- 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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3
- Research group(s)
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-
- Citation count
- 25
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Shows for the first time that ultrasound imaging of muscles at the wrist supports hand gesture classification and measurement with very high precision. The result of a collaboration between medical and HCI researchers, it has been highly influential on recent projects in activity recognition including the Interferi and BeamBand projects at Carnegie Mellon and the SottoVoce project at the University of Tokyo. EchoFlex has also inspired commercial applications in healthcare and prosthetic control, eg cardiac imaging system manufacturer Butterfly Network incorporate the paper in its entirety in US patent 2019/0196600/A1.Awaiting
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -