Human Pose Estimation from Imperfect Sensor Data via the Extended Kalman Filter

Accurate human pose estimation is of vital importance for a variety of human-robot interaction applications, including cooperative task execution, imitation learning and robot-assisted rehabilitation. As robots move from controlled indoor environments to unstructured and outdoor environments, the ability to accurately measure human pose without fixed sensors becomes increasingly important. In this paper, we present a general framework for accurately estimating human pose from a variety of sensors, including body-worn inertial measurement units and cameras, that can be used in indoor and outdoor environments to accurately estimate human pose during arbitrary 3D movements. Using a kinematic model of the human body, the sensor data is fused to estimate the body joint angles and velocities using a constrained Extended Kalman Filter which automatically incorporates feasible joint limits. For periodic movement such as gait, performance can be further improved via online learning of the gait model, individualized to the user. The proposed approach can deal with intermittent data availability and measurement errors during highly dynamic movements.