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Tracking Lower Body 3D Kinematics Using Three IMUs

Sponsored by IEEE Sensors Council (https://ieee-sensors.org/) Title: Tracking Lower Body 3D Kinematics Using Three IMUs Author: Luke Wicent Sy{2}, Nigel Lovell{2}, Stephen Redmond{1} Affiliation: {1}University College Dublin, Ireland; {2}University of New South Wales, Australia Abstract: This paper presents a Lie-group-based extended Kalman filter for estimating pelvis, thigh, shank, and foot kinematics during walking using three IMUs. The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, feet zero-velocity updates, and flat-floor assumption), and constraint update (hinged knee and ankle joints, max leg lengths), with hip, knee, and ankle angle correlation coefficients compared to a camera-based reference of 0.89 ± 0.05, 0.86 ± 0.04, 0.64 ± 0.15. This work contributes toward the development of an ubiquitous remote gait monitoring system. IEEE Sensors Conferences (https://ieee-sensors.org/conferences/) IEEE Sensors Journal (https://ieee-sensors.org/sensors-jour...) IEEE Sensors Letters (https://ieee-sensors.org/sensors-lett...) IEEE Internet of Things Journal (https://ieee-iotj.org/)