Zero-Velocity Detection-A Bayesian Approach to Adaptive Thresholding
- Submitting institution
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University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6411
- Type
- D - Journal article
- DOI
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10.1109/LSENS.2019.2917055
- Title of journal
- IEEE Sensors Letters
- Article number
- 7000704
- First page
- -
- Volume
- 3
- Issue
- 6
- ISSN
- 2475-1472
- Open access status
- Technical exception
- Month of publication
- May
- 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
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4
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Since 2010, when it was shown that most applied zero-velocity detectors can be formulated as likelihood-ratio tests (https://ieeexplore.ieee.org/abstract/document/5523938), the big challenge in foot-mounted inertial navigation has been the calibration of the detection threshold to varying gait conditions. This paper was the first to formulate the problem in Bayesian setting, and to thereby provide a theoretical justification of adaptive zero-velocity detection and efficiently solve the problem of threshold calibration. The model was extended by Professor Andrei M. Shkel’s group (DOI: 10.1109/LSENS.2019.2946129), which proposed to adapt one of the model parameters based on the dynamics during the heel strike.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -