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.
References
[1]
Fong, D.T.P.; Chan, Y.Y. The use of wearable inertial motion sensors in human lower limb biomechanics studies: A systematic review. Sensors 2010, 10, 1556–1565.
[2]
Chelius, G.; Braillon, C.; Pasquier, M.; Horvais, N.; Pissard Gibollet, R.; Espiau, B.; Azevedo-Coste, C. A Wearable sensor network for gait analysis: A six-day experiment of running through the desert. IEEE/ASME Trans. Mechatron. 2011, 16, 878–883.
[3]
Haid, M.; Breitenbach, J. Low cost inertial orientation tracking with Kalman filter. Appl. Math. Comput. 2004, 153, 567–575.
[4]
Tan, U.X.; Veluvolu, K.C.; Latt, W.T.; Shee, C.Y.; Riviere, C.N.; Ang, W.T. Estimating displacement of periodic motion with inertial sensors. IEEE Sens. J. 2008, 8, 1385–1388.
[5]
Mazzà, C.; Donati, M.; McCamley, J.; Picerno, P.; Cappozzo, A. An optimized Kalman filter for the estimate of trunk orientation from inertial sensors data during treadmill walking. Gait Posture 2011, 35, 138–142.
[6]
Bonnet, V.; Mazzà, C.; McCamley, J.; Cappozzo, A. Use of weighted Fourier linear combiner filters to estimate lower trunk 3D orientation from gyroscope sensors data. J. NeuroEng. Rehabil. 2013, 10, 29.
[7]
Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.L.; Shih, H.H.; Zheng, Q.; Yen, N.C.; Tung, C.C.; Liu, H.H. The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 1998, 454, 903–995.
[8]
Flandrin, P.; Goncalvès, P.; Rilling, G. Detrending and Denoising with Empirical Mode Decomposition. Proceedings of the 12th European Signal Processing Conference, Vienna, Austria, 6–10 September 2004; pp. 1581–1584.
[9]
Rilling, G.; Flandrin, P.; Goncalvès, P. On Empirical Mode Decomposition and Its Algorithms. Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP 2003), Grado, Italy, 8–11 June 2003.
[10]
Qian, L.; Xu, G.; Tian, W.; Wang, J. A novel hybrid EMD-based drift denoising method for a dynamically tuned gyroscope (DTG). Measurement 2009, 42, 927–932.
[11]
Zhang, Y.; Wang, S.; Xia, D. EMD-Based Denoising Methods in the MEMS Gyroscope De-Drift. Proceedings of the IEEE International Conference on Nano/Micro Engineered and Molecular Systems, Xiamen, China, 20–23 January 2010; pp. 591–594.
[12]
Iosa, M.; Mazzà, C.; Pecoraro, F.; Aprile, I.; Ricci, E.; Cappozzo, A. Control of the upper body movements during level walking in patients with facio scapula humeral dystrophy. Gait Posture 2010, 31, 68–72.
[13]
Pecoraro, F.; Mazzà, C.; Cappozzo, A.; Thomas, E.E.; Macaluso, A. Reliability of the intrinsic and extrinsic patterns of level walking in older women. Gait Posture 2007, 26, 386–392.
[14]
Adkin, A.L.; Bloem, B.R.; Allum, J.H.J. Trunk sway measurements during stance and gait tasks in Parkinson's disease. Gait Posture 2005, 22, 240–249.
[15]
Mizuike, C.; Ohgi, S.; Morita, S. Analysis of stroke patient walking dynamics using a tri-axial accelerometer. Gait Posture 2009, 30, 60–64.
[16]
Grimpampi, E.; Bonnet, V.; Taviani, A.; Mazzà, C. Estimate of lower trunk angles in pathological gait using gyroscope data. Gait Posture 2013, 38, 523–527.
[17]
Empirical Mode Decomposition. Available online: http://perso.ens-lyon.fr/patrick.flandrin/emd.html (accessed on 25 December 2013).
[18]
Luinge, H. Inertial Sensing of Human Movement. Ph.D. Thesis, Twente University Press, Enschede, The Netherlands, 30 October 2002.
[19]
Rilling, G.; Flandrin, P. On the Influence of Sampling on the Empirical Mode Decomposition. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, France, 14–19 May 2006.
[20]
Moseley, A.; Lanzarone, S.; Bosman, J.; Caplan, B. Ecological validity of walking speed assessment after traumatic brain injury: A pilot study. J. Head Trauma Rehabil. 2004, 19, 341–348.
[21]
Riviere, N.; Rader, R.S.; Thakor, N.V. Adaptive cancelling of physiological tremor for improved precision in microsurgery. IEEE Trans. Biomed. Eng. 1998, 45, 839–846.
[22]
Trnka, P.; Hofreiter, M. The Empirical Mode Decomposition in Real-Time. Proceedings of the 18th International Conference on Process Control, Tatranská Lomnica, Slovakia, 14–17 June 2011; pp. 284–289.
[23]
Guzmán, S.A.; Fischer, M.; Heute, U.; Schmidt, G. Real-Time Empirical Mode Decomposition for EEG Signal Enhancement. Proceedings of the 2013 European Signal Processing Conference (EUSIPCO 2013), Marrakesh, Morocco, 9–13 September 2013.
