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Online Estimation of Bio-mechanical Properties of a Human Driver using Dual Adaptive Unscented Kalman Filter

This paper focuses on online estimation of the human driver’s arm biomechanics in a haptic shared control paradigm wherein a driver and an automation system are physically connected through a motorized steering wheel. The human driver is modeled as a mass-spring-damper system connected to a motion source representing the driver’s intent. This paper proposes using an adaptive Unscented Kalman Filter (UKF) based on a dual estimation structure. The filter structure is composed of two parallel (primary and secondary) UKFs. The primary UKF estimates the states of the adaptive haptic shared control paradigm including the parameters of the human driver biomechanics. The secondary UKF estimates the diagonal elements of the process noise covariance matrix for the primary UKF. By estimating the process noise covariance online, the dual adaptive UKF method is able to compensate the errors resulting from the change of the process noise statistics. To demonstrate the effectiveness, an numerical simulation is performed in the Simulink environment wherein a virtual human driver and the automation system both detect an obstacle but select a different path for avoiding the obstacle. We consider two scenarios wherein the human driver gains control by increasing impedance or yielding control by reducing impedance. We demonstrated that the proposed estimation approach could estimate and track the changes in the parameters of the human biomechanics online.