Understanding Gesture Expressivity through Muscle Sensing
- Submitting institution
-
Goldsmiths' College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2857
- Type
- D - Journal article
- DOI
-
10.1145/2687922
- Title of journal
- ACM Transactions on Computer-Human Interaction
- Article number
- 31
- First page
- -
- Volume
- 21
- Issue
- 6
- ISSN
- 1073-0516
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- URL
-
http://research.gold.ac.uk/id/eprint/11189/
- 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
-
2
- Research group(s)
-
-
- Citation count
- 16
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article presents HCI through physiological signals from user forearm muscle tension. It is significant because it combines quantitative biomedical engineering and qualitative user perception measures in an HCI context. Funded by the ERC grant, MetaGestureMusic (FP7-283771), and ERC Proof-of-Concept grant, BioMusical Instrument. The work has received press coverage including: The Economist’s podcast, Babbage, and Motherboard Vice. The methods have been used by musicians in concert in international venues: WOMAD festival, Vancouver New Music Festival, Mise-en-Scene New York, ZKM Karlsruhe, and Ars Electronica Linz. Two co-authors were awarded a Prix Ars Electronica in 2017.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -