Human activity learning for assistive robotics using a classifier ensemble
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 17 - 702624
- Type
- D - Journal article
- DOI
-
10.1007/s00500-018-3364-x
- Title of journal
- Soft Computing
- Article number
- 3364
- First page
- -
- Volume
- 22
- Issue
- -
- ISSN
- 1432-7643
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2018
- 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
-
4
- Research group(s)
-
A - Computing and Informatics Research Centre
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The contribution of this paper is in introducing the concept of “Fuzzy Transfer Learning” specifically applied to assistive robotics. Once the human activity is understood/learnt, an assistive robot would be able to carry similar tasks. The significance of this paper is that our approach has been validated on experimental dataset created for this work and on a benchmark dataset. A short version of this paper was originally presented in the UK Computational Intelligence, UKCI’2017, in Cardiff University (www.cardiff.ac.uk/conferences/ukci2017) and it was awarded as the best paper. We were invited to present an extended version of our work in this paper.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
