Identification of electrically stimulated quadriceps muscles in paraplegicsubjects

This work establishes a method for the noninvasive in vivo identification o f parametric models of electrically stimulated muscle in paralyzed individu als, when significant inertial loads and/or load transitions are present. T he method used differs from earlier work, in that both the pulse width and stimulus period (interpulse interval) modulation are considered. A Hill-typ e time series model, in which the output is the product of two factors (act ivation and torque-angle) is used. In this coupled model, the activation dy namics depend upon velocity. Sequential nonlinear least squares methods are used in the parameter identification. The ability of the model, using iden tified time-varying parameters, to accurately predict muscle torque outputs is evaluated, along with the variability of the identified parameters. Thi s technique can be used to determine muscle parameter models for biomechani cal computer simulations, and for real-time adaptive control and monitoring of muscle response variations such as fatigue.