Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model

Luh, Jer-Junn; Chang, Guo‐En; Cheng, Cheng‐Kung; Lai, Jin-Shin; Kuo, Te‐Son

doi:10.1016/s1050-6411(98)00030-3

Cited by 101 publications

(55 citation statements)

References 23 publications

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“…In [3], Wang and Buchanan predicted the muscular activations from EMG signals using a four-layer feedforward neural network model trained by a backpropagation learning algorithm. Luh et al built a neural network to model the relationship between EMG activity and elbow joint torque [4]. Liu et al used a neural network to predict dynamic muscle forces from EMG signals [5].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Dynamic Spinal Forces Using a Recurrent Fuzzy Neural Network

Hou

Żurada

Karwowski

et al. 2007

IEEE Trans. Syst., Man, Cybern. B

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Section: Introductionmentioning

confidence: 99%

Estimation of the Dynamic Spinal Forces Using a Recurrent Fuzzy Neural Network

Hou

Żurada

Karwowski

et al. 2007

IEEE Trans. Syst., Man, Cybern. B

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“…18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 According to the above limitations, artificial intelligence has been recruited in this area due to its ability in pattern recognition and signal prediction. For a complete review on neural network application in biomechanics one can refer to (Schöllhorn, 2004).Especially in the field of joint moment prediction, for example, Uchiyama et al, used a three-layer feed forward artificial neural network (FFANN) to predict the elbow joint torque using electromyography (EMG) signals , shoulder and elbow joint angles for constant muscle activation (Uchiyama et al, 1998).Luh et al, also used a three-layer FFANN to predict elbow joint torque using EMG signals , joint angle and elbow joint angular velocity (Luh et al, 1999) . (Wang and Buchanan, 2002) proposed to calculate muscle activities using EMG signals based on a four-layer FFANN.…”

mentioning

confidence: 99%

Human lower extremity joint moment prediction: A wavelet neural network approach

Ardestani

Zhang

Lian

et al. 2014

Expert Systems with Applications

116

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Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics.To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient ( ).Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE<10%, >0.94) compared to FFANN (NRMSE<16%, >0.89).A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation.

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“…Such directly-learning or related methods in machine learning and neural network areas were already verified in simple nonlinear system identification issues [17]. In this work, we employ such learning method to identify parameters in NARX-RNN model (1) rather than use other traditional methods such as gradient-descent iterative methods [18].…”

Section: Narx-rnn Model Identificationmentioning

confidence: 99%

“…Different from NARX model used in [13], the proposed NARX-RNN is established based on a NARX form supplemented with eEMG and torque coupled terms. To enhance convergence of the estimated parameters [16] of such NARX-RNN model, directly-learning pattern [17] is employed [18]. Based on the NARX-RNN model, the prediction results are presented on two SCI patients.…”

Section: Introductionmentioning

confidence: 99%

FES-induced muscular torque prediction with evoked EMG synthesized by NARX-type recurrent neural network

Hayashibe

Zhang

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Functional electrical stimulation (FES) is able to restore motor function of spinal cord injured (SCI) patients. To make adaptive FES control taking into account the actual muscle state with muscular feedback information, torque estimation and prediction are important to be provided beforehand. Evoked EMG (eEMG) has been found to be highly correlated with FES-induced torque under various muscle conditions, indicating that it can be an useful tool for torque/force prediction. To better construct the relationship between eEMG and stimulated muscular torque, nonlinear-arx-type (NARXtype) model is preferred. This paper presents and exploits a NARX-type recurrent neural network (NARX-RNN) model for identification and prediction of FES-induced muscular dynamics with eEMG. Such NARX-RNN model is with a novel architecture for prediction, with robust prediction performance. To make fast convergence for identification of such NARX-RNN, directly-learning pattern is exploited during the learning phase. Due to difficulty of choosing a proper forgetting factor of Kalman filter for predicting time-variant torque with eEMG, such NARX-RNN may be considered to be a better alternative as torque predictor. Data gathered from two SCI patients is used to evaluate the proposed NARX-RNN model. The NARX-RNN model shows promising estimation and prediction performance only based on eEMG.

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Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model

Cited by 101 publications

References 23 publications

Estimation of the Dynamic Spinal Forces Using a Recurrent Fuzzy Neural Network

Estimation of the Dynamic Spinal Forces Using a Recurrent Fuzzy Neural Network

Human lower extremity joint moment prediction: A wavelet neural network approach

FES-induced muscular torque prediction with evoked EMG synthesized by NARX-type recurrent neural network

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