Compressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength
- Submitting institution
-
University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6408
- Type
- D - Journal article
- DOI
-
10.1109/TMC.2017.2706282
- Title of journal
- IEEE Trans. Mob. Comput.
- Article number
- -
- First page
- 293
- Volume
- 17
- Issue
- 2
- ISSN
- 1536-1233
- Open access status
- Technical exception
- Month of publication
- June
- Year of publication
- 2017
- 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
-
7
- Research group(s)
-
-
- Citation count
- 29
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper describes a low-cost, unobtrusive, robust system that supports the independent living by recognising human activities from RFID signals. It was invited for oral presentation at the International Conference on Data Mining, formed an important part of Australian Research Council (ARC) Discovery project, Learning Human Activities through Low Cost, Unobtrusive RFID Technology, and was reported by The Australian newspaper. The research was a foundation for the Digital Health CRC ($200 Million), Australian Commonwealth Government Cooperative Research Centres Program. It was part of the author's PhD thesis, which received the Doctoral Thesis Excellence Award from The University of Adelaide.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
