Accurate Symmetry Calculation with Normalized Dynamic Time Warping Gait Symmetry Ratio
DOI:
https://doi.org/10.15359/ru.36-1.47Keywords:
Wearable Device, Symmetry, Time Series, Dynamic Time Warping (DTW), Gait AsymmetryAbstract
In this paper we propose a new method for symmetry calculation in wearable devices. The problem in this domain is that only discrete features such as stride length, stride duration, or duration of gait phases are used for the symmetry calculation. However, this can lead to failures, since the use of features can result in partial loss of information from the time series. From this we present a possibility to calculate the symmetry by using Dynamic Time Warping (DTW). DTW uses the complete time series for the analysis and is therefore independent of certain features.
References
Andres, R. O., & Stimmel, S. K. (1990). Prosthetic alignment effects on gait symmetry: a case study. Clinical biomechanics, 5(2), 88-96. https://doi.org/10.1016/0268-0033(90)90043-6
Ashhar, K., Soh, C. B., & Kong, K. H. (2017). A wearable ultrasonic sensor network for analysis of bilateral gait symmetry. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4455-4458). IEEE. https://doi.org/10.1109/EMBC.2017.8037845
Blazkiewicz, M., Wiszomirska, I., & Wit, A. (2014). Comparison of four methods of calculating the symmetry of spatial-temporal parameters of gait. Acta of bioengineering and biomechanics, 16(1).
Crea, S., Cipriani, C., Donati, M., Carrozza, M. C., & Vitiello, N. (2014). Providing time-discrete gait information by wearable feedback apparatus for lower-limb amputees: usability and functional validation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(2), 250-257. https://doi.org/10.1109/TNSRE.2014.2365548
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. Ch., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23). https://doi.org/10.1161/01.cir.101.23.e215
Hannink, J., Kautz, T., Pasluosta, C. F., Barth, J., Schülein, S., Gaßmann, K.-G., Klucken, J., & Eskofier, B. M. (2018). Mobile Stride Length Estimation With Deep Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics, 22(2), 354-362. https://doi.org/10.1109/JBHI.2017.2679486
Hassan, M., Kadone, H., Suzyki, K., & Sankai, Y. (2014). Wearable gait measurement system with an instrumented cane for exoskeleton control. Sensors, 14(1), 1705-1722.
Herzog, W., Nigg, B. M., Read, L. J., & Olsson, E. (1989). Asymetries in ground reaction force patterns in nomral human gait. Med Sci Sports Exerc, 21(1), 110–114. https://doi.org/10.1249/00005768-198902000-00020
Hubble, R. P., Naughton, G. A., Silburn, P. A., & Cole, M. H. (2015). Wearable sensor use for assessing standing balance and walking stability in people with Parkinson’s disease: a systematic review. PloS one, 10(4). https://doi.org/10.1371/journal.pone.0123705
Jelén, P., Wit, A., Dudzinski, K., & Nolan, L. (2008). Expressing gait-line symmetry in able-bodied gait. Dynamic Medicine, 7(1), 17. https://doi.org/10.1186/1476-5918-7-17
Jiang, X., Tory, L., Khoshnam, M., Chu, K. H. T., & Menon, C. (2018). Exploration of Gait Parameters Affecting the Accuracy of Force Myography-Based Gait Phase Detection. In 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) (pp. 1205-1210). https://doi.org/10.1109/biorob.2018.8487790
Keogh, E. J., & Ratanamahatana, C. A. (2005). Exact indexing of dynamic time warping. Knowl. Inf. Syst, 7(3). https://doi.org/10.1007/s10115-004-0154-9
Lauziere, S., Betschart, M., Aissaoui, R., & Nadeau, S. (2014). Understanding spatial and temporal gait asymmetries in individuals post stroke. Int J Phys Med Rehabil, 2(3), 201. https://doi.org/10.4172/2329-9096.1000201
Liao, F., Wang, J., & He, P. (2008). Multi-resolution entropy analysis of gait symmetry in neurological degenerative diseases and amyotrophic lateral sclerosis. Medical engineering & physics, 30(3), 299-310. https://doi.org/10.1016/j.medengphy.2007.04.014
Patterson, K. K., Gage, W. H., Brooks, D., Black, S. E., & Mcllroy, W. E. (2010). Evaluation of gait symmetry after stroke: a comparison of current methods and recommendations for standardization. Gait & posture, 31(2), 241-246. https://doi.org/10.1016/j.gaitpost.2009.10.014
Plotnik, M., Giladi, N., & Hausdorff, J. M. (2007). A new measure for quintifying the bilateral coordination of human gait: effects of aging and Parkinson’s disease. Experimental brain research, 181(4), 561-570. https://doi.org/10.1007/s00221-007-0955-7
Sadeghi, H., Allard, P., Prince, F., & Labelle, H. (2000). Symmetry and limb dominance in able-bodied gait: a review. Gait & posture, 12(1), 34-45. https://doi.org/10.1016/s0966-6362(00)00070-9
Steinmetzer, T., Bönninger, I., Reckhardt, M., Reinhardt, F., Erk, D., & Travieso, C. M. (2020). Comparison of algorithms and classifiers for stride detection using wearables. Neural Computing and Applications, 32(24), 17857-17868. https://doi.org/10.1007/s00521-019-04384-6
Steinmetzer, T., Wilberg, S., Bönninger, I., & Travieso, C. M. (2020). Analyzing gait symmetry with automatically synchronized wearable sensors in daily life. Microprocessors and Microsystems, 77, 103118. https://doi.org/10.1016/j.micpro.2020.103118
Yang, C. C., & Hsu, Y. L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors, 10(8), 7772-7788. https://doi.org/10.3390/s100807772
Yang, C. C., Hsu, Y. L., Lu, J. M., Shih, K. S., & Chan, L. (2011. June). Real-time gait cycle parameters recognition using a wearable motion detector. In Proceedings 2011 International Conference on System Science and Engineering (pp. 498-502). IEEE. https://doi.org/10.1109/icsse.2011.5961954
Zifchock, R. A., Davis, I., Higginson, J., & Royer, T. (2008). The symmetry angle: a novel, robust method of quantifying asymmetry. Gait & posture, 27(4), 622-627. https://doi.org/10.1016/j.gaitpost.2007.08.006
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
1. Authors guarantee the journal the right to be the first publication of the work as licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
2. Authors can set separate additional agreements for non-exclusive distribution of the version of the work published in the journal (eg, place it in an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
3. The authors have declared to hold all permissions to use the resources they provided in the paper (images, tables, among others) and assume full responsibility for damages to third parties.
4. The opinions expressed in the paper are the exclusive responsibility of the authors and do not necessarily represent the opinion of the editors or the Universidad Nacional.
Uniciencia Journal and all its productions are under Creative Commons Atribución-NoComercial-SinDerivadas 4.0 Unported.
There is neither fee for access nor Article Processing Charge (APC)