Estimation of Multijoint Stiffness Using Electromyogram and Artificial Neural Network

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...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 39; no. 5; pp. 972 - 980
Main Authors Kim, H.K., Byungduk Kang, Byungchan Kim, Shinsuk Park
Format Journal Article
LanguageEnglish
Published IEEE 01.09.2009
Subjects
Artificial neural network (ANN)
Artificial neural networks
Cybernetics
electromyogram (EMG)
Electromyography
equilibrium point control
Force measurement
Force sensors
Human
Human robot interaction
Impedance
joint stiffness
Manipulators
Mathematical models
Motor drives
Motors
Robot sensing systems
Stiffness
Strategy
Tasks
Turning
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1083-4427
1558-2426
DOI:10.1109/TSMCA.2009.2025021