USING EVOKED EMG AS A SYNTHETIC FORCE SENSOR OF ISOMETRIC ELECTRICALLY STIMULATED MUSCLE

A method for the estimation of the force generated by electrically sti mulated muscle during isometric contraction is developed here, It is b ased upon measurements of the evoked electromyogram (EMG) [EEMG] signa l, Muscle stimulation is provided to the quadriceps muscle of a paraly zed human subject using percutaneous intramuscular electrodes, and EEM G signals are collected using surface electrodes, Through the use of n ovel signal acquisition and processing techniques, as well as a mathem atical model that reflects both the excitation and activation phenomen a involved in isometric muscle force generation, accurate prediction o f stimulated muscle forces is obtained for large time horizons, This a pproach yields synthetic muscle force estimates for both unfatigued an d fatigued states of the stimulated muscle, In addition, a method is d el eloped that accomplishes automatic recalibration of the model to ac count for day-to-day changes in pickup electrode mounting as well as o ther factors contributing to EEMG gain variations, It is demonstrated that the use of the measured EEMG as the input to a predictive model o f muscle torque generation is superior to the use of the electrical st imulation signal as the model input, This is because the measured EEMG signal captures all of the neural excitation, whereas stimulation-to- torque models only reflect that portion of the neural excitation that results directly from stimulation, The time-varying properties of the excitation process cannot be captured by existing stimulation-to-torqu e models, but they are tracked by the EEMG-to-torque models that are d eveloped here, This work represents a promising approach to the real-t ime estimation of stimulated muscle force in functional neuromuscular stimulation applications.