Quantitative comparison of time-varying system identification methods to describe human joint impedance

Accurate and swift tuning of joint impedance is crucial to perform movement and interaction with our environment. Time-varying system identification enables quantification of joint impedance during movement. Many methods have been developed over the years, each with their own mathematical approach and underlying assumptions. Yet, for the...

Revealing time-varying joint impedance with kernel-based regression and nonparametric decomposition

During movements, humans continuously regulate their joint impedance to minimize control effort and optimize performance. Joint impedance describes the relationship between a joint's position and torque acting around the joint. Joint impedance varies with joint angle and muscle activation and differs from trial-to-trial due to inherent...

Analysis of reflex modulation with a biologically realistic neural network