Estimation of multijoint stiffness using electromyogram and artificial neural network

Hyun K. Kim, Byungduk Kang, Byungchan Kim, Shinsuk Park

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)972-980
Number of pages9
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Issue number5
Publication statusPublished - 2009


  • Artificial neural network (ANN)
  • Electromyogram (EMG)
  • Equilibrium point control
  • Joint stiffness

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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