Improving Muscle Force Distribution Model Using Reflex Excitation: Toward a Model-Based Exoskeleton Torque Optimization Approach

Improving Muscle Force Distribution Model Using Reflex Excitation: Toward a Model-Based Exoskeleton Torque Optimization Approach

Blog Article

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, hp 15-ef1005ds 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 spmx50003s50h5 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.