  This paper introduces a new invariant extended Kalman filter design that
produces real-time state estimates and rapid error convergence for the
estimation of the human body movement even in the presence of sensor
misalignment and initial state estimation errors. The filter fuses the data
returned by an inertial measurement unit (IMU) attached to the body (e.g.,
pelvis or chest) and a virtual measurement of zero stance-foot velocity (i.e.,
leg odometry). The key novelty of the proposed filter lies in that its process
model meets the group affine property while the filter explicitly addresses the
IMU placement error by formulating its stochastic process model as Brownian
motions and incorporating the error in the leg odometry. Although the
measurement model is imperfect (i.e., it does not possess an invariant
observation form) and thus its linearization relies on the state estimate,
experimental results demonstrate fast convergence of the proposed filter
(within 0.2 seconds) during squatting motions even under significant IMU
placement inaccuracy and initial estimation errors.
