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Joint Axis Estimation for Fast and Slow Movements Using Weighted Gyroscope and Acceleration Constraints
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: 0000-0001-8185-3117
Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany.
Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany.
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.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Sensor-to-segment calibration is a crucial step in inertial motion tracking. When two segments are connected by a hinge joint, for example in human knee and finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. There exist methods that identify these coordinates by solving an optimization problem that is based on kinematic joint constraints, which involve either the measured accelerations or the measured angular rates. In the current paper we demonstrate that using only one of these constraints leads to inaccurate estimates at either fast or slow motions. We propose a novel method based on a cost function that combines both constraints. The restrictive assumption of a homogeneous magnetic field is avoided by using only accelerometer and gyroscope readings. To combine the advantages of both sensor types, the residual weights are adjusted automatically based on the estimated signal variances and a nonlinear weighting of the acceleration norm difference. The method is evaluated using real data from nine different motions of an upper limb exoskeleton. Results show that, unlike previous approaches, the proposed method yields accurate joint axis estimation after only five seconds for all fast and slow motions.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Inertial sensors, Human movement analysis, Kinematic modeling, Anatomic calibration
National Category
Control Engineering
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
URN: urn:nbn:se:uu:diva-398255OAI: oai:DiVA.org:uu-398255DiVA, id: diva2:1375111
Conference
The 22nd International Conference on Information Fusion (FUSION 2019), Ottawa, Canada, July 2-5, 2019
Funder
Swedish Research Council, 2015-05054Available from: 2019-12-04 Created: 2019-12-04 Last updated: 2019-12-09Bibliographically approved

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CiteExportLink to record
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