Modeling of artificial neural network for the prediction of the multi-joint stiffness in dynamic condition

Byungduk Kang, Byungchan Kim, Shinsuk Park, Hyunkyu Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Unlike robotic systems, humans excel in various tasks by taking advantage of their intrinsic compliance, force sensation, and tactile contact clues. By examining human strategy in arm impedance control, we may be able to teach robotic manipulators human's superior motor skills in contact tasks. This paper develops a novel method for estimating and predicting the human joint impedance using the electro-myogram (EMG) signals and limb position measurements. An artificial neural network (ANN) model was developed to relate the EMG and joint motion to joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. Experimental and simulation results confirmed the feasibility of the developed ANN model.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Pages1840-1845
Number of pages6
DOIs
Publication statusPublished - 2007
Event2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 - San Diego, CA, United States
Duration: 2007 Oct 292007 Nov 2

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Other

Other2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
CountryUnited States
CitySan Diego, CA
Period07/10/2907/11/2

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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    Kang, B., Kim, B., Park, S., & Kim, H. (2007). Modeling of artificial neural network for the prediction of the multi-joint stiffness in dynamic condition. In Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 (pp. 1840-1845). [4399539] (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS.2007.4399539