Sensors 2014, 14(1), 370-381; doi:10.3390/s140100370
Article

Integration of Human Walking Gyroscopic Data Using Empirical Mode Decomposition

1 Movement to Health (M2H) Laboratory, EuroMov, University of Montpellier 1, Montpellier 34090, France 2 LIRMM, University of Montpellier 2, Montpellier 34090, France 3 INRIA, DEMAR-LIRMM, Montpellier 34090, France 4 Department of Mechanical Engineering, University of Sheffield, Sheffield S13JD, UK 5 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; in revised form: 18 December 2013 / Accepted: 18 December 2013 / Published: 27 December 2013
(This article belongs to the Special Issue Wearable Gait Sensors)
PDF Full-text Download PDF Full-Text [3072 KB, Updated Version, uploaded 10 January 2014 16:15 CET]
The original version is still available [2028 KB, uploaded 27 December 2013 09:34 CET]
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.
Keywords: empirical mode decomposition (EMD); inertial measurement unit (IMU); human walking; motion analysis

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

MDPI and ACS Style

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.

AMA Style

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(1):370-381.

Chicago/Turabian Style

Bonnet, Vincent; Ramdani, Sofiane; Azevedo-Coste, Christine; Fraisse, Philippe; Mazzà, Claudia; Cappozzo, Aurelio. 2014. "Integration of Human Walking Gyroscopic Data Using Empirical Mode Decomposition." Sensors 14, no. 1: 370-381.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert