Publikation

Inertial Motion Capture Using Adaptive Sensor Fusion and Joint Angle Drift Correction

Hammad Tanveer Butt, Manthan Pancholi, Mathias Musahl, Pramod Narasimha Murthy, Maria Alejandra Sanchez Marin, Didier Stricker

In: 22nd International Conference on Information Fusion (Fusion-2019), IEEE. International Conference on Information Fusion (FUSION-2019) July 2-5 Ottawa Ontario Canada IEEE 2019.

Abstrakt

The ambulatory motion capture and gait analysis using wearable MEMS based magnetic-inertial measurement units (MIMUs) is challenging despite multi-sensor fusion and effective anatomical (sensor-to-segment) calibration. The MEMS based sensors show degraded performance when run for long time, especially indoors. This is due to the fact that assumption of no acceleration except gravity and homogenous magnetic field no longer holds, when the motion is being performed. The rate gyro is used to complement the accelerometer/ magnetometer for orientation estimation, but integration of its residual biases as well as noise eventually causes the sensor fusion estimates to drift. The errors in heading angle or yaw are particular significant due to persistent nature of magnetic inhomogeneity in the environment. This ultimately results in inaccurate and drifting joint angle estimates between body segments that would require some means of correction. In present work, we propose a new adaptive covariance based EKF for sensor fusion which makes it effectively robust to both dynamic body accelerations as well as inhomogeneous magnetic field. The adaptive covariance method penalizes the bad accelerometer and magnetometer measurements and intelligently updates the gyro biases online using only undisturbed readings of accelerometer/magnetometer. Our sensor fusion algorithm provides accurate orientation estimates for each MIMU node over time. In order to account for any residual drift of joint angles, we propose a novel correction term in our anatomical formulation that performs online correction of drift in individual joint angles and updates it as an orientation offset. This offset correction for joint angle is performed automatically when the limb or extended torso are in neutral quasi-static pose and this condition is judged using accelerometers. Overall our approach achieves precise orientation estimates in highly dynamic conditions and avoids drift or error accumulation due to inhomogeneous magnetic fields during inertial motion capture.

Projekte

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence