Research on Dynamics Parameter Identification of Limb for Rehabilitation Robot

Xiao Bo Guo; Y. Zhai

doi:10.4028/www.scientific.net/KEM.455.585

Paper Titles

Profile Error Synthetic Prediction Model for One-Side Milling of Spatial Cams
p.565

Research on Grinding Technology of Isometric Polygonal Profile
p.571

Research on the Unstability Motion of BTA Deep Hole Boring Bar
p.575

Influence of Original Structure on the Grind-Hardened Layer Structure and its Property of 65Mn Steel
p.580

Research on Dynamics Parameter Identification of Limb for Rehabilitation Robot
p.585

An Operation Model of Fault Diagnosis Based on Grid
p.590

The Study on Application of Worm’s Numerical Control Machining
p.594

Real-Time B-Spline Interpolator with Look-Ahead Scheme for High-Speed CNC Machine Tools
p.599

An Online Modeling Method for Real-Time Thermal Error Compensation on High-Speed Machines Based on RBF Neural Network Theory
p.606

HomeKey Engineering MaterialsKey Engineering Materials Vol. 455Research on Dynamics Parameter Identification of...

Research on Dynamics Parameter Identification of Limb for Rehabilitation Robot

Abstract:

Because of rehabilitants with various characteristics, rehabilitation robot must perceive the rehabilitant states(strength and position) and then adopt corresponding training mode and control strategy. So how to obtain the state of a rehabilitative limb correctly is very significant for a rehabilitation robot during the training. A new method of dynamics parameter identification of limb based on BP (back propagation) artificial neutral network is presented to offer rehabilitation robot dependable information of limb. The simulation results prove that the method of parameter identification can achieve the state of a rehabilitation limb veraciously and robustly. It can suit different rehabilitants at different stages of rehabilitation even if a spasm happens during training.

You might also be interested in these eBooks

Manufacturing Automation Technology Development

View Preview

Info:

Periodical:

Key Engineering Materials (Volume 455)

Pages:

585-589

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.455.585

Citation:

Cite this paper

Online since:

December 2010

Authors:

Xiao Bo Guo, Y. Zhai

Keywords:

BP Artificial Neural Network, Limb Dynamics, Parameter Identification, Rehabilitation Robot, Spasm

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] P.S. Lum, C.G. Burgar, P.C. Shor, M. Majmundar and M.V. Loos: Archives of Physical Medicine and Rehabilitation, vol. 83 (2002) No. 7, pp.952-959.

DOI: 10.1053/apmr.2001.33101

Google Scholar

[2] G. Hagemann, C. Redeckrr, T. Neumann-Haefelin, H.J. Freund and O.W. Witte: Annals of Neurology, vol. 44 (1998) No. 2, pp.255-258.

Google Scholar

[3] S. Fasoli, H. Krebs, J. Stein, W. Frontera and N. Hogan: Archives of Physical Medicine and Rehabilitation, vol. 84 (2003) No. 4, pp.477-482.

DOI: 10.1053/apmr.2003.50110

Google Scholar

[4] H.J. Li and A.G. Song: Robot Technique and Application, vol. 19 (2006) No. 4, pp.32-36.

Google Scholar

[5] S. Hesse, G. Schulte-Tigges, M. Konrad, A. Bardeleben and C. Werner: Archives of Physical Medicine and Rehabilitation, vol. 84 (2003) No. 4, pp.918-920.

Google Scholar

[6] G.Z. Wang: Modern Rehabilitation, vol. 4 (2004) No. 5, pp.650-652.

Google Scholar

[7] D. Erickson and M. Weber: the International Journal of Robotics Research, vol. 22 (2003) No. 1, pp.41-57.

Google Scholar

[8] L. Ljung and T. Sodertstrom: Theory and Practice of Recursive Identification (the MIT Press, American 1983).

Google Scholar

[9] L.J. Love and W.J. Book: IEEE Transactions on Systems, Man, And Cybernetics-Part B: Cybernetics, vol. 34 (2004) No. 1, pp.159-165.

DOI: 10.1109/tsmcb.2003.811756

Google Scholar

[10] L.N. Xu: Neural Networks Control (Publishing House of Electronics Industry, China 2003).

Google Scholar