Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy

Furkan Korkmaz; Abdurrahman Yilmaz; Erhan Akdoʇan; Mehmet Emin Aktan; Murat Atlihan

doi:10.1109/ICMSAO.2015.7152229

Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy

Furkan Korkmaz, Abdurrahman Yilmaz, Erhan Akdoʇan, Mehmet Emin Aktan, Murat Atlihan

Yildiz Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

The aim of this study is to improve the modeling of physiotherapist behaviors on therapy. In order to contribute to a more consistent therapy of the rehabilitation robots used for lower limb, it was aimed that the rehabilitation applications would be made by considering also patient physical information. At this point, the control algorithm of the therapy by means of impedance control has been extended by evaluation of patient physical information can be grouped as weight and length of patient body in addition to force and position (angle) knowledge. The control algorithm using patient physical information as an input was developed by the method of Artificial Neural Networks (ANN) and the architecture of ANN written as multi-layer perceptron (MLP). Also, back propagation learning method is used to train the ANN. The control algorithm computes the impedance parameters by estimating. The proposed method generated successful results in terms of parameter estimation. The obtained results are sufficient for modeling the movements of physiotherapist.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781467366014
DOI'lar	https://doi.org/10.1109/ICMSAO.2015.7152229
Yayın durumu	Yayınlandı - 7 Tem 2015
Harici olarak yayınlandı	Evet
Etkinlik	6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Istanbul, Turkey Süre: 27 May 2015 → 29 May 2015

Yayın serisi

Adı	6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	27/05/15 → 29/05/15

Bibliyografik not

Publisher Copyright:
© 2015 IEEE.

Belgeye Erişim

10.1109/ICMSAO.2015.7152229

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Korkmaz, F., Yilmaz, A., Akdoʇan, E., Aktan, M. E., & Atlihan, M. (2015). Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy. In 6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek Makale 7152229 (6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMSAO.2015.7152229

Korkmaz, Furkan ; Yilmaz, Abdurrahman ; Akdoʇan, Erhan et al. / Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy. 6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek. Institute of Electrical and Electronics Engineers Inc., 2015. (6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek).

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title = "Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy",

abstract = "The aim of this study is to improve the modeling of physiotherapist behaviors on therapy. In order to contribute to a more consistent therapy of the rehabilitation robots used for lower limb, it was aimed that the rehabilitation applications would be made by considering also patient physical information. At this point, the control algorithm of the therapy by means of impedance control has been extended by evaluation of patient physical information can be grouped as weight and length of patient body in addition to force and position (angle) knowledge. The control algorithm using patient physical information as an input was developed by the method of Artificial Neural Networks (ANN) and the architecture of ANN written as multi-layer perceptron (MLP). Also, back propagation learning method is used to train the ANN. The control algorithm computes the impedance parameters by estimating. The proposed method generated successful results in terms of parameter estimation. The obtained results are sufficient for modeling the movements of physiotherapist.",

keywords = "artificial neural networks, Real time impedance parameter estimation, rehabilitation robots",

author = "Furkan Korkmaz and Abdurrahman Yilmaz and Erhan Akdoʇan and Aktan, {Mehmet Emin} and Murat Atlihan",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 ; Conference date: 27-05-2015 Through 29-05-2015",

year = "2015",

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Korkmaz, F, Yilmaz, A, Akdoʇan, E, Aktan, ME & Atlihan, M 2015, Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy. in 6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek., 7152229, 6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek, Institute of Electrical and Electronics Engineers Inc., 6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015, Istanbul, Turkey, 27/05/15. https://doi.org/10.1109/ICMSAO.2015.7152229

Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy. / Korkmaz, Furkan; Yilmaz, Abdurrahman; Akdoʇan, Erhan et al.
6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek. Institute of Electrical and Electronics Engineers Inc., 2015. 7152229 (6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy

AU - Korkmaz, Furkan

AU - Yilmaz, Abdurrahman

AU - Akdoʇan, Erhan

AU - Aktan, Mehmet Emin

AU - Atlihan, Murat

PY - 2015/7/7

Y1 - 2015/7/7

N2 - The aim of this study is to improve the modeling of physiotherapist behaviors on therapy. In order to contribute to a more consistent therapy of the rehabilitation robots used for lower limb, it was aimed that the rehabilitation applications would be made by considering also patient physical information. At this point, the control algorithm of the therapy by means of impedance control has been extended by evaluation of patient physical information can be grouped as weight and length of patient body in addition to force and position (angle) knowledge. The control algorithm using patient physical information as an input was developed by the method of Artificial Neural Networks (ANN) and the architecture of ANN written as multi-layer perceptron (MLP). Also, back propagation learning method is used to train the ANN. The control algorithm computes the impedance parameters by estimating. The proposed method generated successful results in terms of parameter estimation. The obtained results are sufficient for modeling the movements of physiotherapist.

AB - The aim of this study is to improve the modeling of physiotherapist behaviors on therapy. In order to contribute to a more consistent therapy of the rehabilitation robots used for lower limb, it was aimed that the rehabilitation applications would be made by considering also patient physical information. At this point, the control algorithm of the therapy by means of impedance control has been extended by evaluation of patient physical information can be grouped as weight and length of patient body in addition to force and position (angle) knowledge. The control algorithm using patient physical information as an input was developed by the method of Artificial Neural Networks (ANN) and the architecture of ANN written as multi-layer perceptron (MLP). Also, back propagation learning method is used to train the ANN. The control algorithm computes the impedance parameters by estimating. The proposed method generated successful results in terms of parameter estimation. The obtained results are sufficient for modeling the movements of physiotherapist.

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BT - 6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015

Y2 - 27 May 2015 through 29 May 2015

ER -

Korkmaz F, Yilmaz A, Akdoʇan E, Aktan ME, Atlihan M. Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy. In 6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek. Institute of Electrical and Electronics Engineers Inc. 2015. 7152229. (6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015 - Dedicated to the Memory of Late Ibrahim El-Sadek). doi: 10.1109/ICMSAO.2015.7152229

Estimation of impedance control parameters with artificial neural networks for variable robotic resistive therapy

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