Abstract
As a biologic character Human gait is very important for identity recognized, health evaluation, medical monitoring. Gait cycle is one of the most basic parameters in gait analysis. We can easily calculate the gait uniformity, gait symmetry, gait continuity and other parameters based on this parameter. Especially for many of diseases estimation, such as Parkinsons disease, to get the phase synchronization, the precise time of every step event must be determined. We can simply get gait counts from traditional pedometer, but we can not get the precise step interval. In this paper, based on Pan-Tompkins algorithm used in ECG (electrocardiogram) signal, we develop one method using peak detection based on feet acceleration and angular velocity. Experimental results show the method has high precision and less error.
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References
Shi, C., Tian-jun, M., Wan-hong, H.: A multi-layer windows method of moments for gait recognition. J. Electron. Inf. Technol. 31, 116–119 (2009)
Yi, L., Dong, M., Li, F., et al.: Gait recognition method based on hybrid feature in infrared image. Comput. Eng. 37, 1–3 (2011)
Qi, Y., Ding-yu, X.: Gait recognition based on two-scale dynamic bayesian network and more information fusion. J. Electron. Inf. Technol. 34, 1148–1153 (2012)
Dalton, A., Khalil, H., Busse, M., et al.: Analysis of gait and balance through a single triaxial accelerometer in presymptomatic and symptomatic huntingtons disease. Gait Posture 37, 49–54 (2013)
Gafurov, D.: Performance and security analysis of gait-based user authentication[D]. Ph.D. dissertation, University of Oslo (2008)
Yue-xiang, L., Yan, L., Tao, Y., et al.: Multiple classifier based walking pattem recognizing algorithm using acceleration signal. Acta Electronica Sinica 37, 1794–1798 (2009)
Karantonis, D.M., Narayanan, M.R., Mathie, M., et al.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf Technol. Biomed. 10, 156–167 (2006)
Mathie, M.J., Celer, B.G., Lovell Nigel, H., et al.: Classification of basic daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. 42, 679–687 (2004)
Moe Nilssen, R., Helbostad, J.L.: Estimation of gait cycle characteristics of trunk accelerometry. J. Biomech. 37, 121–126 (2004)
Rong, L., Huang, L., Shao-wei, L., et al.: Gait analysis based on gait acceleration. Chin. J. Sens. Actuators 22, 893–896 (2009)
Yang, C.C., Hsu, Y.L., Shih, K.S., et al.: Real-time gait cycle parameters recognition using a wearable motion detector. In: 2011 International Conference on System Science and Engineering, Macao, pp. 498–502 (2011)
Ravi, N., Dandekar, N., Mysore, P., et al.: Activity recognition from accelerometer data. In: Proceedings of the 20th Notional Conference on Artificial Intelligence. Pittsburgh, pp. 1541–1546 (2005)
Ling, X., Ren-fa, L., Juan, L.: Recognition of human activity based on compressed sensing in bady sensor networks. J. Electron. Inf. Technol. 35, 119–125 (2013)
Pan, J., Tompkins, W.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32, 230–236 (1985)
Hamilton, P.S., Tompkins, W.J.: Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng. 33, 1157–1165 (1986)
Yang, Y., Jafari, R., Shankar, S., et al.: Distributed recognition of human actions using wearable motion sensor networks. J. Ambient Intell. Smart Environ. 1, 1–5 (2009)
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Xuan, Y. et al. (2015). Step Cycle Detection of Human Gait Based on Inertial Sensor Signal. In: Sun, L., Ma, H., Fang, D., Niu, J., Wang, W. (eds) Advances in Wireless Sensor Networks. CWSN 2014. Communications in Computer and Information Science, vol 501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46981-1_9
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DOI: https://doi.org/10.1007/978-3-662-46981-1_9
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