EMPress : Practical hand gesture classification with wrist-mounted EMG and pressure sensing
- Submitting institution
-
The University of Bath
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 203022007
- Type
- E - Conference contribution
- DOI
-
10.1145/2858036.2858093
- Title of conference / published proceedings
- CHI '16: Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems
- First page
- 2332
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- May
- Year of publication
- 2016
- URL
-
-
- Supplementary information
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https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F2858036.2858093&file=p2332-mcintosh.mp4&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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5
- Research group(s)
-
-
- Citation count
- 45
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First paper to classify wrist-worn sensing of hand gestures by combining skin pressure and electromyography signals. Identifies that these are complementary and perform well together on the wrist which is ergonomically important for wearable tech developers. Project was an international collaboration with authors from DFKI (German Centre for Artificial Intelligence). Referenced by projects across the US, Japan, Korea, China and Australia, as well as commercial EMG gesture sensing products such as that developed by the CTRL-Labs start-up (directly cited in their CHI 2018 demo) which was in turn acquired by Facebook Reality Labs in 2019.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
