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

Byungduk Kang; Byungchan Kim; Shinsuk Park; Hyunkyu Kim

doi:10.1109/IROS.2007.4399539

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

Byungduk Kang, Byungchan Kim, Shinsuk Park, Hyunkyu Kim

School of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 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 language	English
Title of host publication	Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Pages	1840-1845
Number of pages	6
DOIs	https://doi.org/10.1109/IROS.2007.4399539
Publication status	Published - 2007
Event	2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 - San Diego, CA, United States Duration: 2007 Oct 29 → 2007 Nov 2

Publication series

Name	IEEE International Conference on Intelligent Robots and Systems

Other

Other	2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Country/Territory	United States
City	San Diego, CA
Period	07/10/29 → 07/11/2

ASJC Scopus subject areas

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

Access to Document

10.1109/IROS.2007.4399539

Cite this

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

Modeling of artificial neural network for the prediction of the multi-joint stiffness in dynamic condition. / Kang, Byungduk; Kim, Byungchan; Park, Shinsuk et al.
Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007. 2007. p. 1840-1845 4399539 (IEEE International Conference on Intelligent Robots and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 4399539, IEEE International Conference on Intelligent Robots and Systems, pp. 1840-1845, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007, San Diego, CA, United States, 07/10/29. https://doi.org/10.1109/IROS.2007.4399539

Kang B, Kim B, Park S, Kim H. 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. 2007. p. 1840-1845. 4399539. (IEEE International Conference on Intelligent Robots and Systems). doi: 10.1109/IROS.2007.4399539

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

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