Estimation of Multijoint Stiffness Using Electromyogram and Artificial Neural Network

被引：22

作者：

Kim, Hyun K. ^{[1
]}

Kang, Byungduk ^{[2
]}

Kim, Byungchan ^{[3
]}

Park, Shinsuk ^{[4
]}

机构：

[1] Samsung Elect Co Ltd, Mechatron & Mfg Technol Ctr, Suwon 404370, South Korea

[2] Hyundai Heavy Ind Co Ltd, Ulsan 682792, South Korea

[3] Korea Inst Sci & Technol, Ctr Cognit Robot Res, Seoul 136791, South Korea

[4] Korea Univ, Dept Mech Engn, Seoul 136713, South Korea

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 2009年 / 39卷 / 05期

关键词：

Artificial neural network (ANN); electromyogram (EMG); equilibrium point control; joint stiffness; EQUILIBRIUM-POINT CONTROL; ARM MOVEMENTS; UNSTABLE DYNAMICS; JOINT MOVEMENTS; SYSTEM; MODEL; VISCOELASTICITY; IDENTIFICATION; IMPEDANCE; SINGLE;

D O I：

10.1109/TSMCA.2009.2025021

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：972 / 980

页数：9

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