Human impedance parameter estimation using artificial neural network for modelling physiotherapist motion

• Autorzy: Demir, U. , Kocaoğlu, S. , Akdoğan, E.
• Języki publikacji: EN

Abstrakty

EN

Physiotherapy (physical therapy) is a form of therapy aimed at regaining patients their bodily limb motor functions. The use of what are called therapeutic exercise robots for such purposes is gradually increasing. Therapeutic exercise robots have been developed for lower and upper limbs. These robots lighten the workload of physiotherapists (PTs) by providing the movements on patients' relevant limbs. In order to get robots to perform the movements that the PT expects the patient to perform, it is required to determine the mechanical impedance parameters (inertia, stiffness and damping) due to the contact between the PT and patient's limb's, and to ensure that the robot moves according to these parameters. The aim of this study is to estimate these impedance parameters by using artificial neural networks (ANNs). Data from experiments on real subjects were used to train the network, and success was obtained using new data not presented to the network before. Subsequently, the previously acquired output was re-directed to the network with the purpose of developing a network, which can learn more accurately. Results have provided the designed ANN structure can generate necessary impedance parameter value to imitate PT motions.

Słowa kluczowe

PL

estymacja parametrów; robotyka rehabilitacyjna; sztuczna sieć neuronowa

EN

impedance parameter estimation; rehabilitation robotics; artificial neural network

• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 2
• Strony: 318--326
• Opis fizyczny: Bibliogr. 20 poz., rys., tab., wykr.

Twórcy

autor

Demir, U.

• Marmara University, Institute of Pure and Applied Science, Mechatronics Engineering Department, 34710 İstanbul, Turkey,

autor

Kocaoğlu, S.

• Kırklareli University, Vocational School of Technical Sciences, Electronics & Automation Department, Kırklareli, Turkey,

autor

Akdoğan, E.

• Yıldız Technical University, Mechanical Engineering Faculty, Mechatronics Engineering Department, İstanbul, Turkey,

Bibliografia

• [1] Koyaş E, Saraç M, Erdoğan A, Çetin M, Patoğlu V. Control of a BCI-based upper limb rehabilitation system utilizing posterior probabilities. Signal Processing and Communications Applications Conference (SIU). IEEE; 2013.
• [2] Zhang L, Sun H, Li C. Experiment study of impedance control on horizontal lower limbs rehabilitation robot. IEEE International Conference on Information and Automation; 2010.
• [3] Piovesan D, DiZio P, Lackner JR. A new time-frequency approach to estimate single joint upper limb impedance. 31st Annual International Conference of the IEEE EMBS; 2009.
• [4] Palazzolo JJ, Ferraro M, Krebs HI, Lynch D, Volpe BT, Hogan N. Stochastic estimation of arm mechanical impedance during robotic stroke rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2007;15(1).
• [5] Rouse EJ, Hargrove LJ, Perreault EJ, Kuiken TA. Estimation of human ankle impedance during walking using the perturberator robot. Biomedical Robotics and Biomechatronics (BioRob), 4th IEEE RAS & EMBS International Conference; 2012.
• [6] Satıcı AC, Erdoğan A, Patoğlu V. Design of a reconfigurable ankle rehabilitation robot and its use for the estimation of the ankle impedance. IEEE 11th International Conference on Rehabilitation Robotics; 2009.
• [7] Yuan B, Sekine M, Gonzales J, Tames JG, Yu W. Variable impedance control based on impedance estimation model with EMG signals during extension and flexion tasks for a lower limb rehabilitation robotic system. J Nov Physiother 2013;3(178). http://dx.doi.org/10.4172/2165-7025.1000178.
• [8] Xu G, Song A. Adaptive impedance control based on dynamic recurrent fuzzy neural network for upper-limb rehabilitation robot. IEEE International Conference on Control and Automation; 2009.
• [9] Xu G, Song A. Fuzzy variable impedance control for upper-limb rehabilitation robot. Fifth International Conference on Fuzzy Systems and Knowledge Discovery; 2008.
• [10] Xu G, Song A, Li H. Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural network. J Intell Robot Syst 2011;62:501–25.
• [11] Xu G, Song A, Li H. Control system design for an upper-limb rehabilitation robot. Adv Robot 2011;25(1–2).
• [12] Akdogan E, Tacgin E, Adli MA. Knee rehabilitation using an intelligent robotic system. J Intell Manuf 2009;20(2):195–202.
• [13] Okada S, Sakaki T, Hirata R, Okajima Y, Uchida S, Tomita Y. TEM: a therapeutic exercise machine for the lower extremities of spastic patients. Adv Robot 2001;14(7).
• [14] Akdogan E, Adli M. The design and control of a therapeutic exercise robot for lower limb rehabilitation: Physiotherabot. Mechatronics 2011;21(3):509–22.
• [15] Hogan N. Impedance control: an approach to manipulation, parts I, II, III. J Dyn Syst Meas Control 1985;107:1–23.
• [16] Yoshikawa T. Foundations of robotics: analysis and control. Cambridge: MIT Press; 1990.
• [17] Tsuji T, Tanaka Y. Bio-mimetic impedance control of robotic manipulator for dynamic contact tasks. Robot Auton Syst 2008;56(4):306–16.
• [18] Haykin S. Neural networks: a comprehensive foundation. New York: Macmillan College Publishing Company Inc.; 1994.
• [19] Chen S, Wang Y, Lis S, Wang G, Huang Y, Mao X. Lower limb rehabilitation robot. ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, 2009. London: IEEE; 2009.
• [20] Zhang T, Jiang L, Liu H. A novel grasping force control strategy for multi-fingered prosthetic hand. J Central South Univ 2012;19(6):1537–42.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.

Identyfikatory

DOI

10.1016/j.bbe.2016.01.002