Continuous Identification of Time-Varying Human Impedance During Physical Human-Robot Interaction

Abstract

This paper provides a systemic method for continuously identifying human joint impedance parameters during physical interaction with a robotic system without the need for external sensors. To this end, several identification methods combining payload identification methods with online identification techniques are proposed and compared. The passive behavior of the human joint is modeled by classical spring damper-inertia equations. Monte Carlo simulations are first carried out to compare the expected performance of the proposed methods. Next, experimental validations are conducted on two robotic systems interacting with elements simulating a passive human operator with varying parameters. Results show that the proposed identification combining a separate identification of robot and human parameters with an exponentially-weighted past recursive least squares method gives the best overall results in terms of accuracy. A preliminary example of identifying the wrist mechanical impedance of two able-bodied subjects during f lexion/extension motions is provided. The proposed methods show promising results in continuous monitoring of the human operator's state during physical human-robot interaction, which could be used to detect long-term fatigue or rehabilitation performance.