Sensor data acquisition and processing parameters for human activity classification
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 7110908
- Type
- D - Journal article
- DOI
-
10.3390/s140304239
- Title of journal
- Sensors
- Article number
- -
- First page
- 4239
- Volume
- 14
- Issue
- 3
- ISSN
- 1424-8220
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2014
- 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
-
4
- Research group(s)
-
A - Centre for Healthcare Modelling and Informatics
- Citation count
- 56
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The first rigorous and transferable approach to parameter selection for sensor data sampling and segmentation in Ambient Assisted Living (AAL). Instrumental for accurate processing of sensor inputs for detecting emergencies or monitoring people’s activities to facilitate independent living and wellbeing. Influenced prominent AAL work on fall detection [Hsieh, Sensors 17(2)] and activity recognition [Noor, Pervasive and Mobile Computing 38]; as well as applications in construction [Yang, Automation and Construction 68], agriculture [Shahriar, Computers and Electronics in Agriculture 128], and biomedicine [Hall, PLoS Biology 16(7)].
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -