Estimation of joint impedance using short data segments

Joint impedance is an important property of the human muscular system and plays a role in the control of movement and posture. Previous studies showed that joint impedance varies with the position of the joint and activation level of the surrounding muscles; however, it remains unknown how it varies during movement. Non-parametric algorithms that estimate time-varying impedance do exist; however these algorithms require hundreds of realizations of the same time-varying behavior. In this paper we develop a non-parametric algorithm that can estimate slowly time-varying impedance using multiple short data segments. Using simulated data we evaluate the desired data segment length and the number of realizations needed to yield accurate estimates.