Estimation of multijoint stiffness using electromyogram and artificial neural network

Authors:
Hyun K. Kim;Byungduk Kang;Byungchan Kim;Shinsuk Park
Affiliations:
Mechatronics and Manufacturing Technology Center, Samsung Electronics Company, Ltd., Suwon, Korea;Hyundai Heavy Industries Company, Ltd., Ulsan, Korea;Center for Cognitive Robotics Research, Korea Institute of Science and Technology, Seoul, Korea;Department of Mechanical Engineering, Korea University, Seoul, Korea
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Year:
2009

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Abstract

The human arm exhibits outstanding manipulability in executing various tasks by taking advantage of its intrinsic compliance, force sensation, and tactile contact clues. By examining human strategy in controlling arm impedance, we may be able to understand underlying human motor control and develop control methods for dexterous robotic manipulation. This paper presents a novel method for estimating multijoint stiffness by using electromyogram (EMG) and an artificial neural network model. The artificial network model developed in this paper relates EMG data and joint motion data to joint stiffness. With the proposed method, the multijoint stiffness of the arm was estimated without complex calculation or specialized apparatus. The feasibility of the proposed method was confirmed through experimental and simulation results.