Segmentation of gait cycles using foot-mounted 3D accelerometers

Boutaayamou, Mohamed; Bruls, Olivier; Denoël, Vincent; Schwartz, Cédric; Demonceau, Marie; Garraux, Gaëtan; Verly, Jacques

doi:10.1109/IC3D.2015.7391836

No full text

Paper published in a book (Scientific congresses and symposiums)

Segmentation of gait cycles using foot-mounted 3D accelerometers

Boutaayamou, Mohamed; Bruls, Olivier; Denoël, Vincent et al.

2015 • In Proceedings of the IEEE International Conference on 3D Imaging 2015

Peer reviewed

Permalink
https://hdl.handle.net/2268/188192

DOI
10.1109/IC3D.2015.7391836

Files (0)Send to Details Statistics Bibliography Similar publications

Files

Full Text

No document available.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

3D; segmentation of gait cycles; biomedical signal processing; gait analysis; accelerometers; Parkinson’s disease.

Abstract :

[en] We describe a new gait segmentation method based on the continuous wavelet transform to identify stride-by-stride gait cycles from measurements of foot-mounted three-dimensional (3D) accelerometers. The detection of such gait cycles is indeed a crucial step for an accurate extraction of relevant gait events such as heel strike, toe strike, heel-off, and toe-off. We demonstrate the ability of this segmentation method, used in conjunction with a validated extraction algorithm, to calculate the following gait (duration) parameters for each gait cycle during the gait of a healthy young subject and of an elderly subject with Parkinson’s disease (PD) in OFF and ON states: durations of (1) loading response, (2) mid-stance, (3) push-off, (4) stance, (5) swing, (6) stride, (7) step, and (8) double support phases. The experimental results show that the proposed method can extract relevant refined gait parameters to quantify subtle gait disturbances in subjects with PD.

Research center :

Laboratoire d'Analyse du Mouvement Humain (LAMH) / Signal and Image Exploitation (INTELSIG) - University of Liège, Liège, Belgium

Disciplines :

Electrical & electronics engineering

Author, co-author :

Boutaayamou, Mohamed ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images

Bruls, Olivier ; Université de Liège > Département d'aérospatiale et mécanique > Laboratoire des Systèmes Multicorps et Mécatroniques

Denoël, Vincent ; Université de Liège > Département ArGEnCo > Analyse sous actions aléatoires en génie civil

Schwartz, Cédric ; Université de Liège > Département des sciences de la motricité > Kinésithérapie générale et réadaptation

Demonceau, Marie ; Université de Liège > Département des sciences de la motricité > Kinésithérapie générale et réadaptation

Garraux, Gaëtan ; Université de Liège > Département des sciences biomédicales et précliniques > Biochimie et physiologie du système nerveux

Verly, Jacques ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images

Language :

English

Title :

Segmentation of gait cycles using foot-mounted 3D accelerometers

Publication date :

2015

Event name :

International Conference on 3D Imaging (IC3D) 2015

Event place :

Liège, Belgium

Event date :

14-15 December 2015

Audience :

International

Main work title :

Proceedings of the IEEE International Conference on 3D Imaging 2015

Peer reviewed :

Peer reviewed

Available on ORBi :

since 17 November 2015

Statistics

Number of views

104 (10 by ULiège)

Number of downloads

0 (0 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

Bibliography

Ashton-Miller J. A. Age-associated changes in the biomechanics of gait and gait-related falls in older adults. Neurological Disease and Therapy, 73:63-100, 2005.
J. Verghese, A. LeValley, C. B. Hall, M. J. Katz, A. F. Ambrose, and R. B. Lipton. Epidemiology of gait disorders in community-residing older adults. Journal of the American Geriatrics Society, 54(2):255-61, 2006.
J. Verghese, R. B. Lipton, C. B. Hall, G. Kuslansky, M. J. Katz, and H. Buschke. Abnormality of gait as a predictor of non-Alzheimer's dementia. New England Journal of Medicine, 347(22):1761-1768, 2002.
S. Gillain, E. Warzee, F. Lekeu, V. Wojtasik, D. Maquet, J.-L. Croisier, E. Salmon, and J. Petermans. The value of instrumental gait analysis in elderly healthy, MCI or Alzheimer's disease subjects and a comparison with other clinical tests used in single and dual-task conditions. Annals of Physical and Rehabilitation Medicine, 52(6):453-474, 2009.
H. Stolze, S. Klebe, C. Zechlin, C. Baecker, L. Friege, and G. Deuschl. Falls in frequent neurological diseases: prevalence, risk factors and aetiology. Journal of Neurology, 251(1):79-84, 2004.
I. P. Pappas, M. R. Popovic, T. Keller, V. Dietz, and M. Morari. A reliable gait phase detection system. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 9(2):113-25, 2001.
A. T. M. Willemsen, F. Bloemhof, and H. B. Boom. Automatic stance-swing phase detection from accelerometer data for peroneal nerve stimulation. IEEE Transactions on Biomedical Engineering, 37(12):1201-1208, 1990.
A. Salarian, H. Russmann, F. J. Vingerhoets, C. Dehollain, Y. Blanc, P. R. Burkhard, K. Aminian. Gait assessment in Parkinson's disease: toward an ambulatory system for longterm monitoring. IEEE Transactions on Biomedical Engineering, 51(8):1434-1443, 2004.
S. T. Moore, H. G. MacDougall, J.-M. Gracies, H. S. Cohen, and W. G. Ondo. Long-term monitoring of gait in Parkinson's disease. Gait & Posture, 26(2):200-7, 2007.
J. Stamatakis, J. Crémers, D. Maquet, B. Macq, and G. Garraux. Gait feature extraction in Parkinson's disease using low-cost accelerometers. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 7900-7903, 2011.
K. Aminian, B. Najafi, C. Büla, P. F. Leyvraz, and P. Robert. Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. Journal of Biomechanics, 35:689-699, 2002.
J. Han, H. S. Jeon, B. S. Jeon, and K. S. Park. Gait detection from three dimensional acceleration signals of ankles for the patients with Parkinson's disease. In Proceedings of the IEEE International Special Topic Conference on Information Technology in Biomedicine, 2628, 2006.
A. M. Sabatini, C. Martelloni, S. Scapellato, F. Cavallo. Assessment of walking features from foot inertial sensing. IEEE Transactions on Biomedical Engineering, 52, 486-494, 2005.
A. Mannini and A. M. Sabatini. A hidden Markov modelbased technique for gait segmentation using a foot-mounted gyroscope. In Proceedings of the IEEE Annual International Conference of the Engineering in Medicine and Biology Society, pages 4369-4373, 2011.
E. Guenterberg, A. Y. Yang, H. Ghasemzadeh, R. Jafari, R. Bajcsy, and S. S. Sastry. A method for extracting temporal parameters based on hidden Markov models in body sensor networks with inertial sensors. IEEE Transactions on Information Technology in Biomedicine, 13:1019-1030, 2010.
H. Ying, C. Silex, A. Schnitzer, S. Leonhardt, and M. Schiek. Automatic step detection in the accelerometer signal. In Proceedings of the 4th International Workshop on Wearable and Implantable Body Sensor Networks, pages 80-85, 2007.
T. Brauner, D. Oriwol, T. Sterzing, and T. L. Milani. A single gyrometer inside an instrumented running shoe allows mobile determination of gait cycle and pronation velocity during outdoor running. Footwear Science, 1:25-26, 2009.
J. Barth, C. Oberndorfer, C. Pasluosta, S. Schülein, H. Gassner, S. Reinfelder, P. Kugler, D. Schuldhaus, J. Winkler, J. Klucken, and B. M. Eskofier. Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial sensor data. Sensors, 15:6419-6440, 2015.
M. Sekine, T. Tamura, M. Akay, T. Fujimoto, T. Togawa, and Y. Fukui. Discrimination of walking patterns using wavelet-based fractal analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 10(3):188-196, 2002.
S. Khandelwal, and N. Wickström. Identification of gait events using expert knowledge and continuous wavelet transform analysis. In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pages 197-204, 2014.
M. Yuwono, S. W. Su, B. D. Moulton, and H. T. Nguyen. Unsupervised segmentation of heel-strike IMU data using rapid cluster estimation of wavelet features. In Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 953-956, 2013.
M. Boutaayamou, C. Schwartz, J. Stamatakis, V. Denoël, D. Maquet, B. Forthomme, J.-L. Croisier, B. Macq, J. Verly, G. Garraux, and O. Brüls. Validated extraction of gait events from 3D accelerometer recordings. In IEEE International Conference on 3D Imaging, pages 1-4, 2012.
M. Boutaayamou, C. Schwartz, J. Stamatakis, V. Denoël, D. Maquet, B. Forthomme, J.-L. Croisier, B. Macq, J. Verly, G. Garraux, and O. Brüls. Development and validation of an accelerometer-based method for quantifying gait events. Medical Engineering and Physics, 37:226-232, 2015.
D. Levine, J. Richards, and M. W. Whittle. Whittle's gait analysis. Elsevier Health Sciences, 2012.