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Sensors 2014, 14(1), 370-381;

Integration of Human Walking Gyroscopic Data Using Empirical Mode Decomposition

Movement to Health (M2H) Laboratory, EuroMov, University of Montpellier 1, Montpellier 34090, France
LIRMM, University of Montpellier 2, Montpellier 34090, France
INRIA, DEMAR-LIRMM, Montpellier 34090, France
Department of Mechanical Engineering, University of Sheffield, Sheffield S13JD, UK
Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome 00135, Italy
Author to whom correspondence should be addressed.
Received: 14 November 2013 / Revised: 18 December 2013 / Accepted: 18 December 2013 / Published: 27 December 2013
(This article belongs to the Special Issue Wearable Gait Sensors)
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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. View Full-Text
Keywords: empirical mode decomposition (EMD); inertial measurement unit (IMU); human walking; motion analysis empirical mode decomposition (EMD); inertial measurement unit (IMU); human walking; motion analysis
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Bonnet, V.; Ramdani, S.; Azevedo-Coste, C.; Fraisse, P.; Mazzà, C.; Cappozzo, A. Integration of Human Walking Gyroscopic Data Using Empirical Mode Decomposition. Sensors 2014, 14, 370-381.

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