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Comparative analysis of different adaptive filters for tracking lower segments of a human body using inertial motion sensors
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).ORCID iD: 0000-0003-3363-7414
Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF). Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Occupational and Environmental Medicine.
Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
2013 (English)In: Measurement science and technology, ISSN 0957-0233, E-ISSN 1361-6501, Vol. 24, no 8, 085703- p.Article in journal (Refereed) Published
Abstract [en]

For all segments and tests, a modified Kalman filter and a quasi-static sensor fusion algorithm were equally accurate (precision and accuracy similar to 2-3 degrees) compared to normalized least mean squares filtering, recursive least-squares filtering and standard Kalman filtering. The aims were to: (1) compare adaptive filtering techniques used for sensor fusion and (2) evaluate the precision and accuracy for a chosen adaptive filter. Motion sensors (based on inertial measurement units) are limited by accumulative integration errors arising from sensor bias. This drift can partly be handled with adaptive filtering techniques. To advance the measurement technique in this area, a new modified Kalman filter is developed. Differences in accuracy were observed during different tests especially drift in the internal/external rotation angle. This drift can be minimized if the sensors include magnetometers.

Place, publisher, year, edition, pages
2013. Vol. 24, no 8, 085703- p.
Keyword [en]
movement analysis, inertial measurement unit, Kalman, filter, normalized least mean squares, recursive least mean squares, functional calibration, lower body
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:umu:diva-79893DOI: 10.1088/0957-0233/24/8/085703ISI: 000321943100038OAI: oai:DiVA.org:umu-79893DiVA: diva2:646358
Available from: 2013-09-09 Created: 2013-09-04 Last updated: 2017-12-06Bibliographically approved

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Öhberg, FredrikLundström, RonnieGrip, Helena

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CiteExportLink to record
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  • apa
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  • de-DE
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