Cognitive Learning, Monitoring and Assistance of Industrial Workflows Using Egocentric Sensor Networks
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-191
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0127769
- Title of journal
- PLoS ONE
- Article number
- e0127769
- First page
- -
- Volume
- 10
- Issue
- 6
- ISSN
- 1932-6203
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2015
- 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
- Yes
- Number of additional authors
-
16
- Research group(s)
-
B - AI (Artificial Intelligence)
- Citation count
- 20
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Submitted to PLOS-ONE as suitable venue for this interdisciplinary trans-European collaboration. Provides a system to provide real-time activity recognition from egocentric vision and on-body sensors and augmented-reality feedback to users incorporating a new method for compressing relational descriptions between key objects /hands into the fixed-dimensional observation space of a HMM. Egocentric cameras are becoming ever more ubiquitous and the need for real-time activity recognition from such cameras and/or from on-body sensors has many potential applications from the factory setting in this paper to assistive aids. Several invited/keynote talks based on this work (Cohn/Hogg). Helped Behera obtain faculty position.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
