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Article

Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons

1
Laboratory for Neuromechanics and Biorobotics, Department of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
2
Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(9), 2705; https://doi.org/10.3390/s20092705
Received: 1 April 2020 / Revised: 30 April 2020 / Accepted: 7 May 2020 / Published: 9 May 2020
(This article belongs to the Special Issue Human-Robot Interaction)
Research and development of active and passive exoskeletons for preventing work related injuries has steadily increased in the last decade. Recently, new types of quasi-passive designs have been emerging. These exoskeletons use passive viscoelastic elements, such as springs and dampers, to provide support to the user, while using small actuators only to change the level of support or to disengage the passive elements. Control of such devices is still largely unexplored, especially the algorithms that predict the movement of the user, to take maximum advantage of the passive viscoelastic elements. To address this issue, we developed a new control scheme consisting of Gaussian mixture models (GMM) in combination with a state machine controller to identify and classify the movement of the user as early as possible and thus provide a timely control output for the quasi-passive spinal exoskeleton. In a leave-one-out cross-validation procedure, the overall accuracy for providing support to the user was 86 . 72 ± 0 . 86 % (mean ± s.d.) with a sensitivity and specificity of 97 . 46 ± 2 . 09 % and 83 . 15 ± 0 . 85 % respectively. The results of this study indicate that our approach is a promising tool for the control of quasi-passive spinal exoskeletons. View Full-Text
Keywords: pattern recognition; movement prediction; exoskeleton control; clutched elastic actuators pattern recognition; movement prediction; exoskeleton control; clutched elastic actuators
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MDPI and ACS Style

Jamšek, M.; Petrič, T.; Babič, J. Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons. Sensors 2020, 20, 2705. https://doi.org/10.3390/s20092705

AMA Style

Jamšek M, Petrič T, Babič J. Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons. Sensors. 2020; 20(9):2705. https://doi.org/10.3390/s20092705

Chicago/Turabian Style

Jamšek, Marko, Tadej Petrič, and Jan Babič. 2020. "Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons" Sensors 20, no. 9: 2705. https://doi.org/10.3390/s20092705

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