Predicting EMG Based Elbow Joint Torque Model Using Multiple Input ANN Neurons for Arm Rehabilitation

Jali, Mohd Hafiz; Izzuddin, Tarmizi Ahmad; Bohari, Zul Hasrizal; Sulaima, Mohamad Fani; Sarkawi, Hafez

doi:10.1109/uksim.2014.78

Cited by 14 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jali et al [2] has introduced a model that illustrates the correlation between muscle EMG signal and torque using ANN techniques. For that, EMG signals from biceps muscles are collected and later used to estimate the elbow joint torque temporal values.…”

Section: Related Workmentioning

confidence: 99%

“…In this regard, the ANN is trained using a back propagation neural network (BPNN). The performance of the introduced procedure is determined through calculating the mean square error (MSE) and regression (R 2 ). The proposed model demonstrated an MSE of 1.7638e −11 at epoch 1000 and average regression of 1.000 [2].…”

Section: Related Workmentioning

confidence: 99%

“…Each increment and decrement in the angle are aligned with the sample number of the SEMG signal. The torque for the elbow joint is determined by using the standard torque Equation (11) [2]. τ = (r load F load ) + (r arm F arm ) cos θ (11) where r load is the distance from the elbow joint to the load, F load is known as in Equation 10, F arm is the force due to the mass of the lower arm and r arm is the distance from the elbow joint to the center of mass of the lower arm which is half of r load .…”

Section: Elbow Joint Torque Modelmentioning

confidence: 99%

See 2 more Smart Citations

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

et al. 2019

View full text Add to dashboard Cite

Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Elbow Joint Torque Modelmentioning

confidence: 99%

See 1 more Smart Citation

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Alternatively, the ANN model can extract features on multiple levels of representation and predict very complex functions with a composition of enough representation layers without explicit descriptions of the complex relationships between variables. ANN methods have been applied to estimate lower limb joint torque during a vertical jump [24], isokinetic knee contractions [25], and elbow flexion movements [26]. ANN is, thus, a possible alternative to using an EMG-driven NMS model to map EMG signals to joint torque.…”

Section: Introductionmentioning

confidence: 99%

Ankle Joint Torque Estimation Using an EMG-Driven Neuromusculoskeletal Model and an Artificial Neural Network Model

Zhang

et al. 2021

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

In recent decades, there has been an increasing interest in the use of robotic powered exoskeletons to assist patients with movement disorders in rehabilitation and daily life. Providing assistive torque that compensates for the user's remaining muscle contributions is a growing and challenging field within exoskeleton control. In this article, ankle joint torques were estimated using electromyography (EMG)-driven neuromusculoskeletal (NMS) model and an artificial neural network (ANN) model in seven movement tasks, including fast walking, slow walking, self-selected speed walking, and isokinetic dorsi/plantar flexion at 60 • /s and 90 • /s. In each method, EMG signals and ankle joint angles were used as input, the models were trained with data from 3-D motion analysis, and ankle joint torques were predicted. Six cases using different motion trials as calibration (for the NMS model)/training (for the ANN) were devised, and the agreement between the predicted and measured ankle joint torques was computed. We found that the NMS model could overall better predict ankle joint torques from EMG and angle data than the ANN model with some exceptions; the ANN predicted ankle joint torques with better agreement when trained with data from the same movement. The NMS model predicted ankle joint torque best when calibrated with trials during which EMG reached maximum levels, whereas the ANN predicted well when trained with many trials and types of movements. In addition, the ANN prediction may become less reliable when Manuscript

show abstract

“…Dasanayake W D I G et al [16] utilized independent component analysis (ICA) to isolate the EMG signals from each muscle and proposed a novel kinematic model to measure the actual torque. However, artificial neuron network (ANN) as a computational model based on the structure and functions of biological neural networks, has been still by far the most successful and popular used method in torque prediction [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Ankle Joint Torque Prediction Based on Surface Electromyographic and Angular Velocity Signals

Chen

Jiang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Joint torque prediction plays an important role in quantitative limb rehabilitation assessment and exoskeleton robot, and it is essential to acquire feedback or feedforward signal for adaptive functional electrical stimulation (FES) control. The Surface electromyography (sEMG) signal is one of the basic processing techniques to detect muscle activity, and also one favorable technique to estimate joint torque. In order to predict joint torque in a wide range of real time convenient applications, it is necessary to fuse sEMG signals with other convenient physical sensors such as accelerometers and gyroscopes, herein, we use a time delay artificial neural network to predict human joint force of ankle eversion and inversion based on sEMG and angular velocity signals. We testify our method on the data recorded from 8 subjects (5 healthy subjects and 3 patients) who are on isokinetic ankle eversion and inversion. The results show that the mean Cross-correlation coefficients (ρ) and the mean normalized root-mean-square deviation (NRMSE %) calculated between the prediction and the real value for isokinetic contraction is 0.966±0.019 and 7.9% ±0.026. Compared with artificial neural network (ANN) and support vector regression (SVR), the proposed method can predict the joint torque effectively. For the future application, the method has the potential to be employed to predict the ankle moments in real-time application for quantitative lower limb rehabilitation assessment and exoskeleton robot control.

show abstract

Predicting EMG Based Elbow Joint Torque Model Using Multiple Input ANN Neurons for Arm Rehabilitation

Cited by 14 publications

References 11 publications

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Ankle Joint Torque Estimation Using an EMG-Driven Neuromusculoskeletal Model and an Artificial Neural Network Model

Ankle Joint Torque Prediction Based on Surface Electromyographic and Angular Velocity Signals

Contact Info

Product

Resources

About