Abstract
Gait analysis system constructed of foot-mounted inertial measurement units (IMUs) provides an effective way for estimating gait symmetry. Unfortunately, gait asymmetry may not be visible in a single (spatial or temporal) dimension, and the dual-foot data may be out of phase in the time axis. This paper aims to explore a symmetry analysis method to give a close examination of gait disorders in hemiparesis patients using inertial sensor-based technology. Zero velocity updates (ZUPT)-aided Inertial Navigation System (INS) algorithm is used to estimate foot movements over multiple strides, and the accumulated INS error is further bounded using an inequality-constrained Extended Kalman filter (EKF). Fréchet distance is adopted to align the time series, thereby yielding the symmetry index between dual-foot data. Experiments were conducted along a straight-line path, and both healthy subjects and hemiparesis patients are participated. Experimental results demonstrated that the symmetry analysis with Fréchet distance could reveal gait disorders in both spatial and temporal dimensions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rapp, W., Brauner, T., Weber, L., Grau, S., Mündermann, A., Horstmann, T.: Improvement of walking speed and gait symmetry in older patients after hip arthroplasty: a prospective cohort study. BMC Musculoskelet. Disord. 16(291), 1–8 (2015)
Esser, P., Dawes, H., Collett, J., Howells, K.: Insights into gait disorders: walking variability using phase plot analysis. Parkinson’s Dis. Gait Posture 38(4), 648–652 (2013)
Stock, R., Mork, P.J.: The effect of an intensive exercise programme on leg function in chronic stroke patients: a pilot study with one-year follow-up. Clin. Rehabil. 23(9), 790–799 (2009)
Wang, Z., Zhao, H., Qiu, S., Gao, Q.: Stance-phase detection for ZUPT-aided foot-mounted pedestrian navigation system. IEEE/ASME Trans. Mechatron. 20(6), 3170–3181 (2015)
Lai, D.T.H., Khandoker, A., Begg, R.K., Palaniswami, M.: A hybrid support vector machine and autoregressive model for detecting gait disorders in the elderly. In: Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, pp. 321–326 (2007)
Dillmann, U., Holzhoffer, C., Johann, Y., Bechtel, S., Gräber, S., Massing, C., et al.: Principal component analysis of gait in Parkinson’s disease: relevance of gait velocity. Gait Posture 39(3), 882–887 (2014)
Kale, A., Cuntoor, N., Yegnanarayana, B., Rajagopalan, A.N., Chellappa, R.: Gait analysis for human identification. In: International Conference on Audio- and Video-Based Biometric Person Authentication (2003)
Foxlin, E.: Pedestrian tracking with shoe-mounted inertial sensors. IEEE Comput. Graph. Appl. 25(6), 38–46 (2005)
Nilsson, J.O., Rantakokko, J., Händel, P., Skog, I., Ohlsson, M., Hari, K.V.S.: Accurate indoor positioning of firefighters using dual foot-mounted inertial sensors and inter-agent ranging. In: IEEE/ION Position, Location and Navigation Symposium, vol. 2, pp. 631–636 (2014)
Meldrum, D., Shouldice, C., Conroy, R., Jones, K., Forward, M.: Test–retest reliability of three dimensional gait analysis: including a novel approach to visualising agreement of gait cycle waveforms with Bland and Altman plots. Gait Posture 39(1), 265–271 (2014)
Yen, S.C., Schmit, B.D., Wu, M.: Using swing resistance and assistance to improve gait symmetry in individuals post-stroke. Hum. Mov. Sci. 42(4), 212–224 (2015)
Acknowledgments
This work was supported by Fundamental Research Funds for the Central Universities under Grants DUT16RC(3)015 and DUT15ZD114, and National Natural Science Foundation of China under Grant No. 61473058.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, H., Wang, Z., Qiu, S., Yang, N., Shen, Y. (2019). Examination of Gait Disorders in Hemiparesis Patients Using Foot-Mounted Inertial Sensors. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_239
Download citation
DOI: https://doi.org/10.1007/978-981-10-6571-2_239
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6570-5
Online ISBN: 978-981-10-6571-2
eBook Packages: EngineeringEngineering (R0)