  Understanding human motion is of critical importance for health monitoring
and control of assistive robots, yet many human kinematic variables cannot be
directly or accurately measured by wearable sensors. In recent years, invariant
extended Kalman filtering (InEKF) has shown a great potential in nonlinear
state estimation, but its applications to human poses new challenges, including
imperfect placement of wearable sensors and inaccurate measurement models. To
address these challenges, this paper proposes an augmented InEKF design which
considers the misalignment of the inertial sensor at the trunk as part of the
states and preserves the group affine property for the process model.
Personalized lower-extremity forward kinematic models are built and employed as
the measurement model for the augmented InEKF. Observability analysis for the
new InEKF design is presented. The filter is evaluated with three subjects in
squatting, rolling-foot walking, and ladder-climbing motions. Experimental
results validate the superior performance of the proposed InEKF over the
state-of-the-art InEKF. Improved accuracy and faster convergence in estimating
the velocity and orientation of human, in all three motions, are achieved
despite the significant initial estimation errors and the uncertainties
associated with the forward kinematic measurement model.
