sEMG-based continuous estimation of joint angles of human legs by using BP neural network

Zhang, Feng; Li, Pengfeng; Hou, Zeng-Guang; Lü, Zhen; Chen, Yixiong; Li, Qingling; Tan, Min

doi:10.1016/j.neucom.2011.05.033

Cited by 178 publications

(99 citation statements)

References 33 publications

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“…Common nonlinear regression models, such as ANN, have been frequently built to predict the movements of multi-joints by directly using multi-channel sEMG as inputs [15]- [19]. However, the redundant sEMG-data are always not distinguished, and the prediction errors cannot be evaluated and corrected online by ANN of "open-loop" form as well.…”

Section: Discussion and Future Workmentioning

confidence: 99%

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Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model

Ding

Han

Zhao

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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-Due to the couplings among joint-relative muscles, it is a challenge to accurately estimate continuous multi-joint movements from multi-channel sEMG signals. Traditional approaches always build a nonlinear regression model, such as artificial neural network, to predict the multijoint movement variables using sEMG as inputs. However, the redundant sEMG-data are always not distinguished; the prediction errors cannot be evaluated and corrected online as well. In this work, a correlation-based redundancysegmentation method is proposed to segment the sEMG-vector including redundancy into irredundant and redundant subvectors. Then, a general state-space framework is developed to build the motion model by regarding the irredundant subvector as input and the redundant one as measurement output. With the built statespace motion model, a closed-loop prediction-correction algorithm, i.e., the unscented Kalman filter (UKF), can be employed to estimate the multi-joint angles from sEMG, where the redundant sEMG-data are used to reject model uncertainties. After having fully employed the redundancy, the proposed method can provide accurate and smooth estimation results. Comprehensive experiments are conducted on the multi-joint movements of the upper limb. The maximum RMSE of the estimations obtained by the proposed method is 0.16±0.03, which is significantly less than 0.25±0.06 and 0.27±0.07 (p < 0.05) obtained by common neural networks.

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Section: Discussion and Future Workmentioning

confidence: 99%

“…The sEMG of all channels would then be directly regarded as the inputs of a neural network model [15]- [19]. However, due to the couplings of jointrelative muscles, there exists a redundancy between sEMG from different channels [24].…”

mentioning

confidence: 99%

Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model

Ding

Han

Zhao

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The filtering characteristics of this tissue depend on day to day variation in the position of sEMG electrodes, skin preparation, ambient temperature and electrical impedance. The tissue filtering characteristics are implicitly accounted for by the sEMG to activation filter [6].…”

Section: Fig 3 Flexion and Extension Musclesmentioning

confidence: 99%

The estimation of Knee Joint angle based on Generalized Regression Neural Network (GRNN)

Anwar

Aung

Al-Jumaily

2015

2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)

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Capturing of the intended action of the patient and provide assistance as needed is required in the robotic rehabilitation device. The intended action data that can be extracted from surface Electromyography (sEMG) signal may include the intended posture, intended torque, intended knee joint angle and intended desired impedance of the patient.Utilizing such data to drive robotic assistive device like exoskeleton requires a multilayer control mechanism to achieve a smooth Human Machine Interaction force. It is very important that the controller for gait assistive device is able to extract as many information as possible from the patient muscle with impaired limb and predict different parameters associated with gait cycle. Joint kinematics and dynamics are important to be estimated as the Gait cycle of lower limb consists of flexion and extension postures at knee, hip and ankle joints respectively. This paper proposes a new classification and estimation technique of the lower limb joint kinematics and dynamics based on sEMG signal to predict specifically knee joint flexion and extension postures as well as Knee Joint angles of two postures. In the technique proposed, the feature data of raw sEMG data have been filtered with a second order digital filter and then input to train the Neural Network (NN) and to Generalized Regression Neural Network (GRNN) model to estimate the angle of flexion and extension. The GRNN and NN have been tested with RMS, LOG, MAV, IAV, Hjorth, VAR and MSWT features. GRNN with Multi scale Wavelet Transform (MSWT) feature has ensured 1.5704 Mean Square Error which is very promising accuracy. The SVM has been used to predict postures (flexion and extension). The SVM also has classified flexion and extension with accuracy over 95%.

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“…Zhang et al designed intelligent neural network controller for active rehabilitation device. They used BP neural network for estimation of human knee joint angle change [13]. Prashant et al designed parallel rehabilitation robot for human ankle movement.…”

Section: Introductionmentioning

confidence: 99%

PID Controller Design for Human Elbow Therapy

Eski¹,

Kirnap²,

Kırnap³

2016

International Journal of Applied Mathematics, Electronics and Computers

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Abstract:A controller design for mechatronic system which capable of doing passive therapeutic exercises of patients who have upper extremity limitation is presented in this paper. Expectation from controller is it should produce torque values can exactly repeat degree values depended on time which were taken from first therapy exercises of patients. The designed controller tested with real angle values which was taken from during elbow therapy. Simulation results showed that the proposed control system has good performance at tracking the therapy trajectory. Also that control system may be used for mechatronic upper limb therapy system which can be produced.

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sEMG-based continuous estimation of joint angles of human legs by using BP neural network

Cited by 178 publications

References 33 publications

Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model

Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model

The estimation of Knee Joint angle based on Generalized Regression Neural Network (GRNN)

PID Controller Design for Human Elbow Therapy

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