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.
| Original language | English |
|---|---|
| Pages (from-to) | 972-980 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans |
| Volume | 39 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2009 |
Bibliographical note
Funding Information:Manuscript received August 29, 2008. First published August 14, 2009; current version published August 21, 2009. This work was supported by the Korea Science and Engineering Foundation under Grant R01-2007-000-20977-0 funded by the Korean Ministry of Education, Science and Technology. This paper was recommended by Associate Editor A. A. Maciejewski.
Keywords
- 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