Improving Muscle Force Distribution Model Using Reflex Excitation: Towards A Model-Based Exoskeleton Torque Optimization Approach.

Abstract

To support our hypothesis and the proposed method, we utilize two experimental datasets for exoskeleton torque optimization; passive and active ankle exoskeletons. First, we use the passive exoskeleton dataset to identify the parameters of our model; i.e., reflex gains. Then, to validate the proposed model, the identified parameters are used to optimize the exoskeleton torque profile for the second experimental study. It is assumed that joint kinematic and reflex gains are fixed with and without exoskeleton. 74% of biological torque at the ankle joint cannot be experimentally compensated and the existing models can only explain that 17% of the biological torque is uncompensable. Our improved model can explain that 58% of biological torque is uncompensable (but still 16% remains unexplained). This achievement provides support for our hypothesis and shows undeniable contribution of reflex excitation for exoskeleton torque profile optimization.