Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers

Mohamed Boutaayamou, Vincent Denoël, Olivier Brüls, Marie Demonceau, Didier Maquet, Bénédicte Forthomme, Jean-Louis Croisier, Cédric Schwartz, Jacques G. Verly, Gaëtan Garraux

2016

Abstract

Wearable inertial systems often require many sensing units in order to reach an accurate extraction of temporal gait parameters. Reconciling easy and fast handling in daily clinical use and accurate extraction of a substantial number of relevant gait parameters is a challenge. This paper describes the implementation of a new accelerometer-based method that accurately and precisely detects gait events/parameters from acceleration signals measured from only two accelerometers attached on the heels of the subject’s usual shoes. The first step of the proposed method uses a gait segmentation based on the continuous wavelet transform (CWT) that provides only a rough estimation of motionless periods defining relevant local acceleration signals. The second step uses the CWT and a novel piecewise-linear fitting technique to accurately extract, from these local acceleration signals, gait events, each labelled as heel strike (HS), toe strike (TS), heel-off (HO), toe-off (TO), or heel clearance (HC). A stride-by-stride validation of these extracted gait events was carried out by comparing the results with reference data provided by a kinematic 3D analysis system (used as gold standard) and a video camera. The temporal accuracy ± precision of the gait events were for HS: 7.2 ms ± 22.1 ms, TS: 0.7 ms ± 19.0 ms, HO: −3.4 ms ± 27.4 ms, TO: 2.2 ms ± 15.7 ms, and HC: 3.2 ms ± 17.9 ms. In addition, the occurrence times of right/left stance, swing, and stride phases were estimated with a mean error of −6 ms ± 15 ms, −5 ms ± 17 ms, and −6 ms ± 17 ms, respectively. The accuracy and precision achieved by the extraction algorithm for healthy subjects, the simplification of the hardware (through the reduction of the number of accelerometer units required), and the validation results obtained, convince us that the proposed accelerometer-based system could be extended for assessing pathological gait (e.g., for patients with Parkinson’s disease).

References

  1. Aminian, K., Rezakhanlou, K., De Andres, E., Fritsch, C., Leyvraz, P.-F., and Robert, P. (1999). Temporal feature estimation during walking using miniature accelerometers: an analysis of gait improvement after hip arthroplasty. Medical and Biological Engineering and computing, 37(6):686-691.
  2. Auvinet, B., Chaleil, D., and Barrey, E. (1999). Analyse de la marche humaine dans la pratique hospitalière par une méthode accélérométrique. Revue du Rhumatisme, 66(7-9) :447-457.
  3. Boutaayamou, M., Schwartz, C., Denoël, V., Forthomme, B., Croisier, J.-L., Garraux, G., Verly, J., and Brüls, O. (2014). Development and validation of a 3D kinematic-based method for determining gait events during overground walking. In IEEE International Conference on 3D Imaging, pages 1-6.
  4. Boutaayamou, M., Schwartz, C., Stamatakis, J., Denoël, V., Maquet, D., Forthomme, B., Croisier, J.-L., Macq, B., Verly, J., Garraux, G., and Brüls, O. (2015a). Development and validation of an accelerometerbased method for quantifying gait events. Medical Engineering and Physics, 37:226-232.
  5. Boutaayamou, M., Denoël, V., Verly, J., Garraux, G., and Brüls, O. (2015b). Gait segmentation using continuous wavelet transform for extracting validated gait events from accelerometer signals. In Biomedica 2015-The European Life Sciences Summit.
  6. Godfrey, A., Conway, R., Meagher, D., and ÓLaighin, G. (2008). Direct measurement of human movement by accelerometry. Medical Engineering and Physics, 30(10):1364-1386.
  7. Forsman, P. M., Toppila, E. M., and Haeggström, E. O. (2009). Wavelet analysis to detect gait events. In IEEE EMBC, pages 424-427.
  8. Khandelwal, S., and Wickström, N. (2014). 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.
  9. Lee, J.-.A., Cho S.-H., Lee J.-W., Lee K.-H., and Yang H.- K. (2007). Wearable accelerometer system for measuring the temporal parameters of gait. In Proceedings of the 29th Annual International Conference of the IEEE EMBS, pages 23-26.
  10. Rampp, A., Barth, J., Schülein, S., Gaßmann, K.-G., Klucken, J., and Eskofier, B. M. (2015). Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE Transactions on Biomedical Engineering, 62(4):1089-1097.
  11. Rueterbories, J., Spaich., E.G., Larsen, B., and Andersen O.K. (2010). Methods for gait event detection and analysis in ambulatory systems. Medical Engineering and Physics, 32(6):545-552.
  12. Salarian, A., Russmann, H., Vingerhoets, F.J.G., Dehollain, C., Blanc, Y., Burkhard P.R., and Aminian, K. (2004). Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring. IEEE Transactions on Biomedical Engineering, 51(8):1434-1443.
  13. Selles, R.W., Formanoy, M.A.G., Bussmann, J.B.J., Janssens, P.J., and Stam, H.J. (2005). Automated estimation of initial and terminal contact timing using accelerometers; development and validation in transtibial amputees and controls. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(1):81-88.
  14. Stamatakis, J., Crémers, J., Maquet, D., Macq, B., and Garraux, G. (2011). 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.
  15. Whittle, W. (1996). Clinical gait analysis: a review. Human Movement Science, 15:369-387.
  16. Willemsen, A.T.M., Bloemhof, F., and Boom, H.B. (1990). Automatic stance-swing, phase detection from accelerometer data for peroneal nerve stimulation. IEEE Transactions on Biomedical Engineering, 37(12):1201-8.
Download


Paper Citation


in Harvard Style

Boutaayamou M., Denoël V., Brüls O., Demonceau M., Maquet D., Forthomme B., Croisier J., Schwartz C., Verly J. and Garraux G. (2016). Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 57-66. DOI: 10.5220/0005696900570066


in Bibtex Style

@conference{biosignals16,
author={Mohamed Boutaayamou and Vincent Denoël and Olivier Brüls and Marie Demonceau and Didier Maquet and Bénédicte Forthomme and Jean-Louis Croisier and Cédric Schwartz and Jacques G. Verly and Gaëtan Garraux},
title={Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)},
year={2016},
pages={57-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005696900570066},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)
TI - Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers
SN - 978-989-758-170-0
AU - Boutaayamou M.
AU - Denoël V.
AU - Brüls O.
AU - Demonceau M.
AU - Maquet D.
AU - Forthomme B.
AU - Croisier J.
AU - Schwartz C.
AU - Verly J.
AU - Garraux G.
PY - 2016
SP - 57
EP - 66
DO - 10.5220/0005696900570066