Fully Automatic Analysis of Engagement and Its Relationship to Personality in Human-Robot Interactions
- Submitting institution
-
University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1908
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2016.2614525
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 705
- Volume
- 5
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- January
- 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
- No
- Number of additional authors
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4
- Research group(s)
-
-
- Citation count
- 30
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Fully automatic prediction of nonverbal behaviours is still a challenge in the field of HRI due to lack of data. The strength of this paper lies in its ability to automatically predict user engagement by introducing the concept of individual vs. interpersonal features and group engagement, and modelling the relationship between engagement and personality. It is the main outcome of the collaboration with Institut des Systèmes Intelligents et de Robotique (UPMC, France). Led to popularising the MHHRI Dataset (requested by top international researchers in the field) and the award of the prestigious 5-year EPSRC Fellowship (EP/R030782/1 - £1.06m FEC).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -