Electromyographic (EMG) signal to joint torque processing and effect of various factors on EMG to torque model

This study present electromyographic (EMG) signal to torque model and investigates the effects of various factors on EMG signal and EMG to torque model. Pre-processing techniques are applied on EMG signal in order to remove the DC offset, 60 Hz noise and to estimate the EMG amplitude. The estimated EMG amplitude is then mapped to joint torque using a new non-linear equation. This equation uses some parameters, whose values are obtained using nonlinear regression. Ten subjects took part in the experiments and performed variable force maximal voluntary contractions (MVC) and submaximal voluntary contractions (SMVC). The resulting elbow joint torque and EMG signals were preprocessed and entered to the model to find value of the parameters using nonlinear regression. Once these values were obtained they were put into the model and thus joint torque was estimated. Also EMG is analysed for effect of various factors like muscle fatigue, cross talk and different joint velocity. The results obtained from this model are highly correlated with the true values of the torque and the average correlation and mean square error for different experiments are 0.9997 and 0.047 Nm respectively. This new mathematical equation can be used to design a control system for rehabilitation and wearable robots.

[1]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[2]  P. Rack,et al.  The effects of length and stimulus rate on tension in the isometric cat soleus muscle , 1969, The Journal of physiology.

[3]  A. Ronald Gallant,et al.  Seemingly unrelated nonlinear regressions , 1975 .

[4]  Neville Hogan,et al.  Myoelectric Signal Processing: Optimal Estimation Applied to Electromyography - Part II: Experimental Demonstration of Optimal Myoprocessor Performance , 1980, IEEE Transactions on Biomedical Engineering.

[5]  J. van den Berg,et al.  EMG to force processing III: Estimation of model parameters for the human triceps surae muscle and assessment of the accuracy by means of a torque plate. , 1981, Journal of biomechanics.

[6]  T. Zwemer,et al.  Effect of myoelectric signal processing on the relationship between muscle force and processed EMG , 1986 .

[7]  R. Kass.  Nonlinear Regression Analysis and its Applications , 1990 .

[8]  F E Zajac,et al.  Muscle coordination of movement: a perspective. , 1993, Journal of biomechanics.

[9]  D. Winter,et al.  Models of recruitment and rate coding organization in motor-unit pools. , 1993, Journal of neurophysiology.

[10]  J. Mizrahi,et al.  A musculotendon model of the fatigue profiles of paralyzed quadriceps muscle under FES , 1993, IEEE Transactions on Biomedical Engineering.

[11]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[12]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[13]  S. Gandevia,et al.  Fatigue brought on by malfunction of the central and peripheral nervous systems. , 1995, Advances in experimental medicine and biology.

[14]  J. Quintern,et al.  A physiologically based model of muscle activation verified by electrical stimulation , 1997 .

[15]  S. Gandevia,et al.  Twitch interpolation in human muscles: mechanisms and implications for measurement of voluntary activation. , 1999, Journal of neurophysiology.

[16]  S. Metral,et al.  Relationship between force and integrated EMG activity during voluntary isometric anisotonic contraction , 2004, European Journal of Applied Physiology and Occupational Physiology.

[17]  Manolis I. A. Lourakis.  A Brief Description of the Levenberg-Marquardt Algorithm Implemented by levmar , 2005 .

[18]  Günter Hommel,et al.  Predicting the intended motion with EMG signals for an exoskeleton orthosis controller , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Roberto Merletti,et al.  Electromyography. Physiology, engineering and non invasive applications , 2005 .

[20]  Stephen H M Brown,et al.  Co-activation alters the linear versus non-linear impression of the EMG-torque relationship of trunk muscles. , 2008, Journal of biomechanics.