Improving Muscle Force Distribution Model Using Reflex Excitation: Toward a Model-Based Exoskeleton Torque Optimization Approach - Mojtaba Rayati; Rezvan Nasiri; Majid Nili Ahmadabadi (IEEE, 2023)
In this study, we improve the existing model for force distribution over the muscles by considering reflex excitation as a nonvoluntary mechanism of our neuromuscular system. The improved model can explain the large difference between biological torque and experimentally optimized assistive torque profiles. Accordingly, we hypothesize that the “nonvoluntary nature of reflexive excitation highly restricts biological torque compensation”. The proposed model can also potentially characterize co-activation behavior in antagonistic muscles. Using our improved model, we introduce a well-posed framework to optimize the exoskeleton torque profile by metabolic rate minimization. Methods: 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. Limitations: It is assumed that joint kinematic and reflex gains are fixed with and without exoskeleton. Results: 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.