Identification of ankle joint stiffness from short segments of data: Application to passive dynamics during movement

This paper presents a state-space (subspace) method for identification of parallel-cascade joint stiffness from short segments of data. It provides unbiased estimates of stiffness by accounting for the contributions of initial conditions of each segment. The method is important in situations where it is not possible to acquire a long stationary data due to switching or time-varying behavior. The power of the method was demonstrated by using it to efficiently characterize ankle joint stiffness through the joint´s range of motion.