Abstract
Due to intensive human-robot interaction in exoskeletons, it is desirable to design and control the exoskeletons with behavior compatible to human limbs to achieve natural and energy-efficient motion assistance. This requires both novel variable stiffness actuators and also technologies able to detect human muscle stiffness, upon which exoskeleton joint stiffness can be tuned. In this paper, a novel stiffness modulation method is proposed to achieve variable stiffness with respect to human status. The method is developed upon a novel actuator of nonlinear variable stiffness. Moreover, human joint stiffness is estimated innovatively from Force Myography (FMG) signals. The correlation between the recorded FMG and human joint stiffness is established with Machine Learning methods, which is further used for online estimation. An elbow joint exoskeleton is finally developed and tested. The results validate experimentally the methods for bionic compatible stiffness modulation.
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