Continuous estimation of ankle joint angular position based on the myoelectric signals

Rahmatian, Sajjad; Mahjoob, M. J.; Hanachi, Mohammad Reza

doi:10.1109/rios.2016.7529507

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Błażkiewicz and Wit [15] developed an artificial neural network able to accurately simulate the changes in the angle of the ankle, knee and hipjoints during the gait cycle. Rahmatian et al [16] used neural networks to design joint models with surface EMG as input and ankle joint angle and velocity as output. The model approximated velocities of the joint opening and closing by time-delayed artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.INDEX TERMS Functional electrical stimulation, ankle angle, Hammerstein model, neural network, genetic algorithm.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Results for the linear model show similar precision values to those achieved by other authors (PANG et al, 2015;LIU;HERZOG;SAVELBERG, 1999;RAHMATIAN;MAHJOOB;HANACHI, 2016;MAMIKOGLU et al, 2016). For most of the simulations, the model achieved correlations above 90% and RMSE under 20 • .…”

Section: Part V Discussion Conclusion and Future Work 8 Discussionsupporting

confidence: 84%

EMG-driven exoskeleton control.

Sommer¹

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The need for mechanisms that assist human movements has been increasing due to the rising number of people that has some kind of movement disability. In this scenario, it is of great importance the development of control methods that assist the interface between a motor assistive device and its user. This work proposes a controller for an exoskeleton with one degree of freedom, using surface electromyography signals from the user as the input signal. An exoskeleton was adapted to serve as platform for the developed control method. To create an EMG-to-Angle model, a set of experiments were carried out with six subjects. The experiment consisted of a series of continuous and discrete elbow flexion and extension movements with different load levels. Using the experimental data, linear (ARIMAX) and non linear (Hammerstein-Wiener) system identification methods were evaluated to determine which is the best candidate for the estimation of the EMG-to-Angle model, based on its accuracy and ease of implementation. A new experiment was conducted to develop a real-time controller, based on FIR model and tested in a real-time application. Tests showed that the controller is capable of estimating the elbow joint angle with correlation above 70% and root-mean-square error below 25 • when compared to the measured elbow joint angles.

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Development of a neural network based control algorithm for powered ankle prosthesis

Keleş

Yücesoy

2020

Journal of Biomechanics

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Continuous estimation of ankle joint angular position based on the myoelectric signals

Cited by 7 publications

References 14 publications

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

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

EMG-driven exoskeleton control.

Development of a neural network based control algorithm for powered ankle prosthesis

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