A mathematical model for mapping EMG signal to joint torque for the human elbow joint using nonlinear regression

2009 4th International Conference on Autonomous Robots and Agents

K. Ullah, Jung-Hoon Kim

Research output: Contribution to conferencePaper

Abstract

Numerous researchers have investigated the relationship between EMG and joint torque. Most of these studies use some conventional filtering (i.e. rectification followed by low pass filtering) to estimate the electromyogram (EMG) amplitude and then relate it to the joint torque. Currently some advanced pre-processing techniques (i.e. signal whitening) are also used to estimate the EMG amplitude and then relate it to joint torque. In this study we apply some pre-processing techniques like DC offset removal, noise filtering followed by rectification and then we calculate the moving average of the EMG signal. Thus we get a linear envelope (muscle activation) of the EMG signal and use that linear envelope to estimate the joint torque. To map the EMG to joint torque we propose a new mathematical model. This model has some unknown adjustable parameters, and the values of these parameters are obtained using nonlinear regression. Five subjects took part in the experiments. They were asked to perform non-fatiguing and variable force maximal voluntary contractions (MVC) and submaximal voluntary contractions (SMVC), and the resulting elbow joint torque and EMG signals were recorded. This recorded data was entered to the model, to estimate best fit values for the unknown parameters. Once these values of the parameters were obtained they were put into the model and thus joint torque was estimated. Predictions made by our model are well correlated with experimental data in both MVC and SMVC, the correlation coefficient and mean square error obtained for experimental data during MVC are 0.998 and 0.056 Nm respectively. The results of this new model were compared with other existing models and some new models and it was found that our model has greater correlation and least mean square error with experimental data. This model may be helpful in the control systems for recognition systems, robot manipulators, exoskeletons, EMG prosthesis and electric stimulators.
Original languageEnglish
Pages103-108
Number of pages6
DOIs
Publication statusPublished - 2000

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Autonomous agents
Torque
Robots
Mathematical models
Mean square error
Processing
Manipulators
Muscle
Chemical activation
Control systems

Cite this

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abstract = "Numerous researchers have investigated the relationship between EMG and joint torque. Most of these studies use some conventional filtering (i.e. rectification followed by low pass filtering) to estimate the electromyogram (EMG) amplitude and then relate it to the joint torque. Currently some advanced pre-processing techniques (i.e. signal whitening) are also used to estimate the EMG amplitude and then relate it to joint torque. In this study we apply some pre-processing techniques like DC offset removal, noise filtering followed by rectification and then we calculate the moving average of the EMG signal. Thus we get a linear envelope (muscle activation) of the EMG signal and use that linear envelope to estimate the joint torque. To map the EMG to joint torque we propose a new mathematical model. This model has some unknown adjustable parameters, and the values of these parameters are obtained using nonlinear regression. Five subjects took part in the experiments. They were asked to perform non-fatiguing and variable force maximal voluntary contractions (MVC) and submaximal voluntary contractions (SMVC), and the resulting elbow joint torque and EMG signals were recorded. This recorded data was entered to the model, to estimate best fit values for the unknown parameters. Once these values of the parameters were obtained they were put into the model and thus joint torque was estimated. Predictions made by our model are well correlated with experimental data in both MVC and SMVC, the correlation coefficient and mean square error obtained for experimental data during MVC are 0.998 and 0.056 Nm respectively. The results of this new model were compared with other existing models and some new models and it was found that our model has greater correlation and least mean square error with experimental data. This model may be helpful in the control systems for recognition systems, robot manipulators, exoskeletons, EMG prosthesis and electric stimulators.",
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