Integration of Human Walking Gyroscopic Data Using Empirical Mode Decomposition

Vincent Bonnet 1, * Sofiane Ramdani 2 Christine Azevedo Coste 3 Philippe Fraisse 3 Claudia Mazza 1 Aurelio Cappozzo 1
* Auteur correspondant
3 DEMAR - Artificial movement and gait restoration
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The present study was aimed at evaluating the Empirical Mode Decomposition (EMD) method to estimate the 3D orientation of the lower trunk during walking using the angular velocity signals generated by a wearable inertial measurement unit (IMU) and notably flawed by drift. The IMU was mounted on the lower trunk (L4-L5) with its active axes aligned with the relevant anatomical axes. The proposed method performs an offline analysis, but has the advantage of not requiring any parameter tuning. The method was validated in two groups of 15 subjects, one during overground walking, with 180°turns, and the other during treadmill walking, both for steady-state and transient speeds, using stereophotogrammetric data. Comparative analysis of the results showed that the IMU/EMD method is able to successfully detrend the integrated angular velocities and estimate lateral bending, flexion-extension as well as axial rotations of the lower trunk during walking with RMS errors of 1 deg for straight walking and lower than 2.5 deg for walking with turns.
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Sensors, MDPI, 2014, 14 (1), pp.370-381. 〈10.3390/s140100370〉
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Vincent Bonnet, Sofiane Ramdani, Christine Azevedo Coste, Philippe Fraisse, Claudia Mazza, et al.. Integration of Human Walking Gyroscopic Data Using Empirical Mode Decomposition. Sensors, MDPI, 2014, 14 (1), pp.370-381. 〈10.3390/s140100370〉. 〈lirmm-00922705〉

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