Inertial measurement units are an efficient tool to estimate the orientation of a rigid body with respect to a global or navigation frame. Thanks to their relatively small scale, these devices are often employed in clinical environments in form of wearable devices. A direct consequence of this large use of inertial sensors has been the development of many sensor fusion techniques for pose estimation in many practical applications. In this paper we study the feasibility of a nonlinear "Unscented" variant of the well-known Kalman Filter for gyroscope/accelerometer sensor fusion in pelvis pose estimation during treadmill walking. In addition, orientation estimation has been obtained without IMU magnetometer data, in order to propose a method suitable also for environments where magnetic disturbances could arise. Pelvis heading (yaw), bank (roll) and attitude (pitch) angles have been evaluated both using the proposed filter and a gold standard optometric system. The root mean square errors obtained using the proposed sensor fusion with respect to the gold standard are below 1 degree for each axis, showing also a significant high correlation (> 0.90). Findings of this study highlight the suitability of a magnetometer-free UKF approach for pose estimation of pelvis during human walking on treadmill, providing information useful also for further estimation of center of mass displacement in the same experimental conditions.

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http://dx.doi.org/10.1109/EMBC.2019.8857914DOI Listing

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