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Open AccessArticle

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors

1
Systems and Control, Department of Information Technology, Uppsala University, SE-75105 Uppsala, Sweden
2
Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands
3
Control Systems Group, Technische Universität Berlin, 10623 Berlin, Germany
4
Department of Mechatronics, Campus Estado de Mexico, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3534; https://doi.org/10.3390/s20123534
Received: 17 April 2020 / Revised: 29 May 2020 / Accepted: 16 June 2020 / Published: 22 June 2020
(This article belongs to the Special Issue Human and Animal Motion Tracking Using Inertial Sensors)
Inertial motion capture relies on accurate sensor-to-segment calibration. When two segments are connected by a hinge joint, for example in human knee or finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. Methods for estimating the joint axis using accelerations and angular rates of arbitrary motion have been proposed, but the user must perform sufficiently informative motion in a predefined initial time window to accomplish complete identifiability. Another drawback of state of the art methods is that the user has no way of knowing if the calibration was successful or not. To achieve plug-and-play calibration, it is therefore important that 1) sufficiently informative data can be extracted even if large portions of the data set consist of non-informative motions, and 2) the user knows when the calibration has reached a sufficient level of accuracy. In the current paper, we propose a novel method that achieves both of these goals. The method combines acceleration- and angular rate information and finds a globally optimal estimate of the joint axis. Methods for sample selection, that overcome the limitation of a dedicated initial calibration time window, are proposed. The sample selection allows estimation to be performed using only a small subset of samples from a larger data set as it deselects non-informative and redundant measurements. Finally, an uncertainty quantification method that assures validity of the estimated joint axis parameters, is proposed. Experimental validation of the method is provided using a mechanical joint performing a large range of motions. Angular errors in the order of 2 were achieved using 125–1000 selected samples. The proposed method is the first truly plug-and-play method that overcome the need for a specific calibration phase and, regardless of the user’s motions, it provides an accurate estimate of the joint axis as soon as possible. View Full-Text
Keywords: inertial measurement units; gyroscopes and accelerometers; sensor-to-segment calibration; kinematic constraints; joint axis identification; validation on mechanical joint inertial measurement units; gyroscopes and accelerometers; sensor-to-segment calibration; kinematic constraints; joint axis identification; validation on mechanical joint
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MDPI and ACS Style

Olsson, F.; Kok, M.; Seel, T.; Halvorsen, K. Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors. Sensors 2020, 20, 3534. https://doi.org/10.3390/s20123534

AMA Style

Olsson F, Kok M, Seel T, Halvorsen K. Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors. Sensors. 2020; 20(12):3534. https://doi.org/10.3390/s20123534

Chicago/Turabian Style

Olsson, Fredrik; Kok, Manon; Seel, Thomas; Halvorsen, Kjartan. 2020. "Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors" Sensors 20, no. 12: 3534. https://doi.org/10.3390/s20123534

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