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Zero-velocity detection: A Bayesian approach to adaptive thresholding
Department of Computer Science, University of Oxford, Oxford, U.K..
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. S3 Research AB, Stockholm, Sweden.ORCID iD: 0000-0002-3054-6413
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-3270-171X
Department of Computer Science, University of Oxford, Oxford, U.K..
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2019 (English)In: IEEE Sensors Letters, E-ISSN 2475-1472, Vol. 3, no 6, article id 7000704Article in journal (Refereed) Published
Abstract [en]

A Bayesian zero-velocity detector for foot-mounted inertial navigation systems is presented. The detector extends existing zero-velocity detectors based on the likelihood-ratio test and allows, possibly time-dependent, prior information about the two hypotheses-the sensors being stationary or in motion-to be incorporated into the test. It is also possible to incorporate information about the cost of a missed detection or a false alarm. Specifically, we consider a hypothesis prior based on the velocity estimates provided by the navigation system and an exponential model for how the cost of a missed detection increases with the time since the last zero-velocity update. Thereby, we obtain a detection threshold that adapts to the motion characteristics of the user. Thus, the proposed detection framework efficiently solves one of the key challenges in current zero-velocity-aided inertial navigation systems: the tuning of the zero-velocity detection threshold. A performance evaluation on data with normal and fast gait demonstrates that the proposed detection framework outperforms any detector that chooses two separate fixed thresholds for the two gait speeds.

Place, publisher, year, edition, pages
IEEE Sensors Council, 2019. Vol. 3, no 6, article id 7000704
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-179671DOI: 10.1109/LSENS.2019.2917055ISI: 000722244000008Scopus ID: 2-s2.0-85076705828OAI: oai:DiVA.org:liu-179671DiVA, id: diva2:1598486
Available from: 2021-09-29 Created: 2021-09-29 Last updated: 2024-10-24Bibliographically approved

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Skog, IsaacGustafsson, Fredrik

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf