Hierarchical syntactic models for human activity recognition through mobility traces
- Submitting institution
-
University of Keele
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 352
- Type
- D - Journal article
- DOI
-
10.1007/s00779-019-01319-9
- Title of journal
- Personal and Ubiquitous Computing
- Article number
- -
- First page
- 451
- Volume
- 24
- Issue
- 4
- ISSN
- 1617-4909
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2019
- URL
-
https://link.springer.com/article/10.1007/s00779-019-01319-9
- 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
-
3
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- From an ongoing collaboration with University of Kentucky and Missouri University of Science and Technology (MST), Ortolani defined the hierarchical automata models for trajectories and the relative similarity measure, and was an adviser for Casella's PhD. The grammatical inference framework is a novel approach to intepretable knowledge, focusing on practical use -- previous research seeks generic structure in data (e.g. https://doi.org/f6w8bv), or represents data using artificial grammars (https://doi.org/fgdh).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -