Discovery and recognition of emerging human activities using a hierarchical mixture of directional statistical models
- Submitting institution
-
University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 258172666
- Type
- D - Journal article
- DOI
-
10.1109/TKDE.2019.2905207
- Title of journal
- IEEE Transactions on Knowledge and Data Engineering
- Article number
- -
- First page
- 1304
- Volume
- 32
- Issue
- 7
- ISSN
- 1041-4347
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- 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
-
2
- Research group(s)
-
C - Health Informatics
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The ways in which people perform activities changes over time, which defeats many machine learning algorithms for recognising human activities from sensor data. The work presented introduces techniques from directional statistics that improve classifier accuracy while massively reducing data collection and annotation overheads when evaluated against standard datasets.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
