Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection
- Submitting institution
-
The University of Kent
- Unit of assessment
- 12 - Engineering
- Output identifier
- 14464
- Type
- D - Journal article
- DOI
-
10.1016/j.inffus.2018.09.001
- Title of journal
- Information Fusion
- Article number
- -
- First page
- 46
- Volume
- 49
- Issue
- -
- ISSN
- 1566-2535
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2018
- URL
-
https://kar.kent.ac.uk/68930/
- 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)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes the adoption of deep-learning approaches for effective human emotion classification using a large number of sensors input (smartphones and wristbands) which allows analysing human emotion ‘in the field’, as opposed to controlled experimental settings. The paper has led to a project funded by the State Hospital in collaboration with University of Edinburgh to explore the use of wearables to detect agitation among psychiatric patients, as well as a project funded by NHS England working with Northampton NHS Trusts to use wristbands to detect depression.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -