Subtractive clustering Takagi-Sugeno position tracking for humans by low-cost inertial sensors and velocity classification

dc.contributor.authorMaton, Dariusz
dc.contributor.authorEconomou, John T.
dc.contributor.authorGalvão Wall, David
dc.contributor.authorWard, David
dc.contributor.authorTrythall, Simon
dc.date.accessioned2023-04-18T11:14:26Z
dc.date.available2023-04-18T11:14:26Z
dc.date.issued2023-04-12
dc.description.abstractIn this work, open-loop position tracking using low-cost inertial measurement units is aided by Takagi-Sugeno velocity classification using the subtractive clustering algorithm to help generate the fuzzy rule base. Using the grid search approach, a suitable window of classified velocity vectors was obtained and then integrated to generate trajectory segments. Using publicly available experimental data, the reconstruction accuracy of the method is compared against four competitive pedestrian tracking algorithms. The comparison on selected test data, has demonstrated more competitive relative and absolute trajectory error metrics. The proposed method in this paper is also verified on an independent experimental data set. Unlike the methods which use deep learning, the proposed method has shown to be transparent (fuzzy rule base). Lastly, a sensitivity analysis of the velocity classification models to perturbations from the training orientation at test time is investigated, to guide developers of such data-driven algorithms on the granularity required in an ensemble modelling approach. The accuracy and transparency of the approach may positively influence applications requiring low-cost inertial position tracking such as augmented reality headsets for emergency responders.en_UK
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC): EP/S513623/1 BAE Systemsen_UK
dc.identifier.citationMaton D, Economou JT, Galvão Wall D, et al., (2023) Subtractive clustering Takagi-Sugeno position tracking for humans by low-cost inertial sensors and velocity classification. Measurement and Control, Volume 56, Issue 9-10, November 2023, pp. 1534-1544en_UK
dc.identifier.issn0020-2940
dc.identifier.urihttps://doi.org/10.1177/00202940231155496
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19499
dc.language.isoenen_UK
dc.publisherSageen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectInertial measurement uniten_UK
dc.subjectdata miningen_UK
dc.subjectmachine learningen_UK
dc.subjectnavigationen_UK
dc.subjectmotion trackingen_UK
dc.subjectwearablesen_UK
dc.titleSubtractive clustering Takagi-Sugeno position tracking for humans by low-cost inertial sensors and velocity classificationen_UK
dc.typeArticleen_UK

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