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Pregled bibliografske jedinice broj: 1026878

Estimating the Ankle Angle Induced by FES via the Neural Network-Based Hammerstein Model


(Hrvatsko-kineski bilateralni projekt znanstveno-tehnološke suradnje) Zhou, Hai Yan; Huang, Lin Ke; Gao, Yue Ming; Lučev Vasić, Željka; Cifrek, Mario; Du, Min
Estimating the Ankle Angle Induced by FES via the Neural Network-Based Hammerstein Model // IEEE access, 7 (2019), 141277-141286 doi:10.1109/access.2019.2943453 (međunarodna recenzija, članak, znanstveni)


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Naslov
Estimating the Ankle Angle Induced by FES via the Neural Network-Based Hammerstein Model

Autori
Zhou, Hai Yan ; Huang, Lin Ke ; Gao, Yue Ming ; Lučev Vasić, Željka ; Cifrek, Mario ; Du, Min

Kolaboracija
Hrvatsko-kineski bilateralni projekt znanstveno-tehnološke suradnje

Izvornik
IEEE access (2169-3536) 7 (2019); 141277-141286

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Functional electrical stimulation ; ankle angle ; Hammerstein model ; neural network ; genetic algorithm

Sažetak
Functional electrical stimulation (FES) has been widely used in limb rehabilitation. The first step for the precision rehabilition is to clarify the variation of limb angle induced by FES. In this study, an electric stimulator and an inertial sensor are used to build a human body experimental platform. Motion characteristics of ankle angle induced by electrical stimulation pulse variation are obtained through experiment. The obtained ankle angle characteristics are used to train a neural network-based Hammerstein (H) model and the model parameters are identified by the genetic algorithm, which can effectively predict the ankle angle change induced by electrical stimulation. The structural parameters of the H model are adjusted according to the normalized root mean square error value (NRMSE) of the training data. The 10-fold cross-validation is used to verify the feasibility and effectiveness of the model. Experimental results show that the neural network-based H model can effectively predict the output change of the ankle angle induced by the electrical stimulation pulse, and its root mean square error (RMSE) and NRMSE are 2.78 ± 0.33° and 23.70 ± 1.77%, respectively. Therefore, the proposed model can provide a theoretical basis for predicting ankle angle change in an electrical stimulation closed-loop control system.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Mario Cifrek (autor)

Avatar Url Željka Lučev Vasić (autor)

Citiraj ovu publikaciju

(Hrvatsko-kineski bilateralni projekt znanstveno-tehnološke suradnje) Zhou, Hai Yan; Huang, Lin Ke; Gao, Yue Ming; Lučev Vasić, Željka; Cifrek, Mario; Du, Min
Estimating the Ankle Angle Induced by FES via the Neural Network-Based Hammerstein Model // IEEE access, 7 (2019), 141277-141286 doi:10.1109/access.2019.2943453 (međunarodna recenzija, članak, znanstveni)
(Hrvatsko-kineski bilateralni projekt znanstveno-tehnološke suradnje) (Hrvatsko-kineski bilateralni projekt znanstveno-tehnološke suradnje) Zhou, H., Huang, L., Gao, Y., Lučev Vasić, Ž., Cifrek, M. & Du, M. (2019) Estimating the Ankle Angle Induced by FES via the Neural Network-Based Hammerstein Model. IEEE access, 7, 141277-141286 doi:10.1109/access.2019.2943453.
@article{article, year = {2019}, pages = {141277-141286}, DOI = {10.1109/access.2019.2943453}, keywords = {Functional electrical stimulation, ankle angle, Hammerstein model, neural network, genetic algorithm}, journal = {IEEE access}, doi = {10.1109/access.2019.2943453}, volume = {7}, issn = {2169-3536}, title = {Estimating the Ankle Angle Induced by FES via the Neural Network-Based Hammerstein Model}, keyword = {Functional electrical stimulation, ankle angle, Hammerstein model, neural network, genetic algorithm} }
@article{article, year = {2019}, pages = {141277-141286}, DOI = {10.1109/access.2019.2943453}, keywords = {Functional electrical stimulation, ankle angle, Hammerstein model, neural network, genetic algorithm}, journal = {IEEE access}, doi = {10.1109/access.2019.2943453}, volume = {7}, issn = {2169-3536}, title = {Estimating the Ankle Angle Induced by FES via the Neural Network-Based Hammerstein Model}, keyword = {Functional electrical stimulation, ankle angle, Hammerstein model, neural network, genetic algorithm} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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