Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons

Sensors

卷:20

Tadej Petrič¹  Marko Jamšek¹  Jan Babič¹ 

[1] Laboratory for Neuromechanics and Biorobotics, Department of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia;

关键词: pattern recognition;    movement prediction;    exoskeleton control;    clutched elastic actuators;   

DOI  :  10.3390/s20092705

来源: DOAJ

【 摘 要 】

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 $86.72\pm 0.86$ % (mean ± s.d.) with a sensitivity and specificity of $97.46\pm 2.09$ % and $83.15\pm 0.85$ % respectively. The results of this study indicate that our approach is a promising tool for the control of quasi-passive spinal exoskeletons.

【 授权许可】

Unknown