Stochastic estimation of arm mechanical impedance during robotic stroke rehabilitation

This paper presents a stochastic method to estimate the multijoint mechanical impedance of the human arm suitable for use in a clinical setting, e.g., with persons with stroke undergoing robotic rehabilitation for a paralyzed arm. In this context, special circumstances such as hypertonicity and tissue atrophy due to disuse of the hemiplegic limb must be considered. A low-impedance robot was used to bring the upper limb of a stroke patient to a test location, generate force perturbations, and measure the resulting motion. Methods were developed to compensate for input signal coupling at low frequencies apparently due to human-machine interaction dynamics. Data was analyzed by spectral procedures that make no assumption about model structure. The method was validated by measuring simple mechanical hardware and results from a patient's hemiplegic arm are presented.