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.

翻译：人类运动对健康监测和控制辅助机器人至关重要,但许多人类运动变量无法直接或精确地用磨损传感器来测量。 近年来,变异型的延长卡尔曼过滤器(InEKF)在非线性国家估计中显示出巨大的潜力,但其对人类的应用带来了新的挑战,包括不完善地放置磨损传感器和不准确的测量模型。为了应对这些挑战,本文件建议扩大InEKF设计,该设计考虑到作为州一部分的中继器惯性传感器在中继器上的不匹配,并保护该组在工艺模型上的近距离属性。 个人化的低深度前向运动模型(InEKF)已经建成,并被用作扩大的InEKF的测量模型。 新的InEKF设计的观察性分析提出了新的挑战,包括不完善地放置可磨损传感器和不准确性测量模型和梯子移动运动。 实验结果证实拟议的InEKF在作为州的一部分对工艺的状态的优异性表现。 在估计与人类前向方向相关的模型的所有初步误差方面,尽管已经实现了重大的精确性和方向。