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Tremor Quantification through Event-based Movement Trajectory Modeling
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology. (Embedded Systems)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology. (Embedded Systems)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.ORCID iD: Embedded Systems
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0002-6608-250x
2017 (English)In: 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017), IEEE, 2017, p. 542-547Conference paper, Published paper (Refereed)
Abstract [en]

A simple non-intrusive approach to tremor quantification utilizing the repetitive nature of the phenomenon is proposed and implemented on a portable device equipped with a fused off-the-shelf sensor platform measuring 3D acceleration. The device can be automatically activated when picked up from a stationary position and acceleration measurements are performed for a certain time interval. This usage scenario naturally arises e.g. when a person lifts the cellular phone from a surface to the ear to make or answer a call. The relatively slow and damped voluntary movement is separated by filtering from the involuntary and repetitive tremor manifestations in the device position. Extreme points of the tremor signal are detected and the time stamps of the corresponding events are used to estimate of the momentary tremor amplitude and frequency. Kalman filtering of the estimates is applied further to obtain their smoothed versions.

Place, publisher, year, edition, pages
IEEE, 2017. p. 542-547
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-357053DOI: 10.1109/CCTA.2017.8062518ISI: 000426981500088ISBN: 978-1-5090-2183-3 (print)ISBN: 978-1-5090-2182-6 (electronic)ISBN: 978-1-5090-2181-9 (print)OAI: oai:DiVA.org:uu-357053DiVA, id: diva2:1239662
Conference
1st Annual IEEE Conference on Control Technology and Applications, 27-30 Aug. 2017, Mauna Lani, HI, USA.
Available from: 2018-08-17 Created: 2018-08-17 Last updated: 2018-08-17Bibliographically approved

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