0
Research Papers

Using Entropy Measures to Characterize Human Locomotion

[+] Author and Article Information
Graham Leverick

Department of Mechanical Engineering,
University of Manitoba,
Winnipeg, MB R3T 5V6, Canada

Tony Szturm

School of Medical Rehabilitation,
University of Manitoba,
Winnipeg, MB R3E 0T6, Canada
e-mail: tony.szturm@med.umanitoba.ca

Christine Q. Wu

Department of Mechanical Engineering,
University of Manitoba,
Winnipeg, MB R3T 5V6, Canada
e-mail: christine.wu@umanitoba.ca

Manuscript received January 27, 2014; final manuscript received August 7, 2014; accepted manuscript posted August 27, 2014; published online October 17, 2014. Assoc. Editor: Paul Rullkoetter.

J Biomech Eng 136(12), 121002 (Oct 17, 2014) (8 pages) Paper No: BIO-14-1052; doi: 10.1115/1.4028410 History: Received January 27, 2014; Revised August 07, 2014; Accepted August 27, 2014

Entropy measures have been widely used to quantify the complexity of theoretical and experimental dynamical systems. In this paper, the value of using entropy measures to characterize human locomotion is demonstrated based on their construct validity, predictive validity in a simple model of human walking and convergent validity in an experimental study. Results show that four of the five considered entropy measures increase meaningfully with the increased probability of falling in a simple passive bipedal walker model. The same four entropy measures also experienced statistically significant increases in response to increasing age and gait impairment caused by cognitive interference in an experimental study. Of the considered entropy measures, the proposed quantized dynamical entropy (QDE) and quantization-based approximation of sample entropy (QASE) offered the best combination of sensitivity to changes in gait dynamics and computational efficiency. Based on these results, entropy appears to be a viable candidate for assessing the stability of human locomotion.

FIGURES IN THIS ARTICLE
<>
Copyright © 2014 by ASME
Topics: Entropy
Your Session has timed out. Please sign back in to continue.

References

Su, J. L., and Dingwell, J. B., 2007, “Dynamic Stability of Passive Dynamic Walking on an Irregular Surface,” ASME J. Biomech. Eng., 129(6), pp. 802–810. [CrossRef]
Garcia, M., Chatterjee, A., Ruina, A., and Coleman, M., 1998, “The Simplest Walking Model: Stability, Complexity, and Scaling,” ASME J. Biomech. Eng., 120(2), pp. 281–288. [CrossRef]
Bruijn, S. M., Meijer, O. G., Beek, P. J., and van Dieen, J. H., 2013, “Assessing the Stability of Human Locomotion: A Review of Current Measures,” J. R. Soc. Interface, 10(83), pp. 1–23. [CrossRef]
Leverick, G., Szturm, T., and Wu, C. Q., 2013, “Investigation of the Suitability of Utilizing Permutation Entropy to Characterize Gait Dynamics,” ASME Paper No. DSCC2013-3895. [CrossRef]
Dingwell, J., Cusumano, J., Sternad, D., and Cavanagh, P., 2000, “Slower Speeds in Patients With Diabetic Neuropathy Lead to Improved Local Dynamic Stability of Continuous Overground Walking,” J. Biomech., 33(10), pp. 1269–1277. [CrossRef] [PubMed]
Szturm, T., Maharjan, P., Marotta, J., Shay, B., Shrestha, S., and Sakhalkar, V., 2013, “The Interacting Effect of Cognitive and Motor Task Demands on Performance of Gait, Balance and Cognition in Young Adults,” Gait Posture, 38(4), pp. 596–602. [CrossRef] [PubMed]
Szturm, T., Marotta, J., Wu, C. Q., and Nayak, A., 2013, “Technology-Assisted and Motivational Program for a Blended Approach to Prevent and Manage Balance, Gaze, Mobility and Cognitive Decline With Age,” OA Evidence-Based Med., 1(1), pp. 1–7. [CrossRef]
Owings, T., and Grabiner, M., 2004, “Step Width Variability, but Not Step Length Variability or Step Time Variability, Discriminates Gait of Healthy Young and Older Adults During Treadmill Locomotion,” J. Biomech., 37(6), pp. 935–938. [CrossRef] [PubMed]
Arif, M., Ohtaki, Y., Nagatomi, R., Ishihara, T., and Inooka, H., 2002, “Analysis of the Effect of Fatigue on Walking Gait Stability,” Proceedings of 2002 International Symposium on Micromechatronics and Human Science, Nagoya, Japan, Oct. 20–23 , pp. 253–258. [CrossRef]
Arif, M., Ohtaki, Y., Nagatomi, R., and Inooka, H., 2004, “Estimation of the Effect of Cadence on Gait Stability in Young and Elderly People Using Approximate Entropy Technique,” Meas. Sci. Rev., 4(2), pp. 29–40. Available at: http://www.measurement.sk/2004/S2/Arif.pdf
McGregor, S., and Bollt, E., 2012, “Control Entropy: What is It and What Does It Tell Us?” Clin. Kinesiol., 66(1), pp. 7–12. Available at: http://people.clarkson.edu/~ebollt/Papers/McGregor%26Bollt_66_1_7-12.pdf
Sun, S., 2010, “Complexity Analysis of the Gait Time Series Using Fine-Grained Permutation Entropy,” Proceedings of the Sixth International Conference on Natural Computation, Yantai, China, Aug. 10–12, pp. 3878–3879. [CrossRef]
Costa, M., Peng, C., Goldberger, A., and Hausdorff, J., 2003, “Multiscale Entropy Analysis of Human Gait Dynamics,” Physica A, 330(1–2), pp. 53–60. [CrossRef]
Cai, S., Zhou, P., Yang, H., Zhou, T., Wang, B., and Zhao, F., 2007, “Diffusion Entropy Analysis on the Stride Interval Fluctuation of Human Gait,” Physica A, 375(2), pp. 687–692. [CrossRef]
Karmakar, C., Khandoker, A., Begg, R., Palaniswami, M., and Taylor, S., 2007, “Understanding Ageing Effects by Approximate Entropy Analysis of Gait Variability,” Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France, Aug. 23–26, pp. 1965–1968. [CrossRef]
Khandoker, A., Palaniswami, M., and Begg, R., 2008, “A Comparative Study on Approximate Entropy Measure and Poincare Plot Indexes of Minimum Foot Clearance Variability in the Elderly During Walking,” J. Neuroeng. Rehabil., 5, p. 4. [CrossRef] [PubMed]
Bandt, C., and Pompe, B., 2002, “Permutation Entropy: A Natural Complexity Measure for Time Series,” Phys. Rev. Lett., 88(17), p. 174102. [CrossRef] [PubMed]
Leverick, G., Wu, C. Q., and Szturm, T., “Coarse Quantization in Calculations of Entropy Measures for Experimental Time Series,” Nonlinear Dyn. (in press). [CrossRef]
Richman, J., and Moorman, J., 2000, “Physiological Time-Series Analysis Using Approximate and Sample Entropy,” Am. J. Physiol.: Heart Circ. Physiol., 278(6), pp. 2039–2049. Available at: http://ajpheart.physiology.org/content/278/6/H2039.short
Chen, W., Zhuang, J., Yu, W., and Wang, Z., 2008, “Measuring Complexity Using Fuzzyen, Apen, and Sampen,” Med. Eng. Phys., 31(1), pp. 61–68. [CrossRef] [PubMed]
Leverick, G., 2013, “Entropy Measures in Dynamical Systems and Their Viability in Characterizing Bipedal Walking Gait Dynamics,” MSc thesis, University of Manitoba, Winnipeg, MB, Canada.
Al-Yahya, E., Dawes, H., Smith, L., Dennis, A., Howells, K., and Cockburn, J., 2010, “Cognitive Motor Interference While Walking: A Systematic Review and Meta-Analysis,” Neurosci. Biobehav. Rev., 35(3), pp. 715–728. [CrossRef] [PubMed]
Chinn, S., 1991, “Repeatability and Method Comparison,” Thorax, 46(6), pp. 454–456. [CrossRef] [PubMed]
Young, L., 2003, Entropy, Princeton University Press, Princeton, NJ, pp. 313–328.
Sun, Y., and Wu, C., 2012, “Stability Analysis Via the Concept of Lyapunov Exponents: A Case Study in Optimal Controlled Biped Standing,” Int. J. Control, 85(12), pp. 1952–1966. [CrossRef]
van Schooten, K., Sloot, L., Bruijn, S., Kingma, H., Meijer, O., Pijnappels, M., and van Dieen, J., 2011, “Sensitivity of Trunk Variability and Stability Measures to Balance Impairments Induced by Galvanic Vestibular Stimulation During Gait,” Gait Posture, 33(4), pp. 656–660. [CrossRef] [PubMed]
Strang, A., Haworth, J., Hieronymus, M., Walsh, M., and Smart, L., 2011, “Structural Changes in Postural Sway Lend Insight Into Effects of Balance Training, Vision, and Support Surface on Postural Control in a Healthy Population,” Eur. J. Appl. Physiol., 111(7), pp. 1485–1495. [CrossRef] [PubMed]
Rigoldi, C., Cimolin, V., Camerota, F., Celletti, C., Albertini, G., Mainardi, L., and Galli, M., 2013, “Measuring Regularity of Human Postural Sway Using Approximate Entropy and Sample Entropy in Patients With Ehlers-Danlos Syndrome Hypermobility Type,” Res. Dev. Disabil., 34(2), pp. 840–846. [CrossRef] [PubMed]
Callisaya, M., Blizzard, L., Schmidt, M., Martin, K., McGinley, J., Sanders, L., and Srikanth, V., 2011, “Gait, Gait Variability and the Risk of Multiple Incident Falls in Older People: A Population-Based Study,” Age Ageing, 40(4), pp. 481–487. [CrossRef] [PubMed]
Lamoth, C., van Deudekom, F., van Campen, J., Appels, B., de Vries, O., and Pijnappels, M., 2011, “Gait Stability and Variability Measures Show Effects of Impaired Cognition and Dual Tasking in Frail People,” J. Neuroeng. Rehabil., 8, p. 2. [CrossRef] [PubMed]
Rafiq, M., McGovern, A., Jones, S., Harris, K., Tomson, C., Gallagher, H., and de Lusignan, S., 2014, “Falls in the Elderly Were Predicted Opportunistically Using a Decision Tree and Systematically Using a Database-Driven Screening Tool,” J. Clin. Epidemiol., 67(8), pp. 877–886. [CrossRef] [PubMed]
Muir, S. W., Gopaul, K., and Montero Odasso, M. M., 2012, “The Role of Cognitive Impairment in Fall Risk Among Older Adults: A Systematic Review and Meta-Analysis,” Age Ageing, 41(3), pp. 299–308. [CrossRef] [PubMed]
Owings, T. M., and Grabiner, M. D., 2003, “Measuring Step Kinematic Variability on an Instrumented Treadmill: How Many Steps are Enough?” J. Biomech., 36(8), pp. 1215–1218. [CrossRef] [PubMed]

Figures

Grahic Jump Location
Fig. 1

Passive dynamic walking model

Grahic Jump Location
Fig. 2

Experimental setup

Grahic Jump Location
Fig. 3

Segmented and normalized pressure mat data: (a) medial–lateral and (b) anterior–posterior

Grahic Jump Location
Fig. 4

Entropy versus perturbation size in walker model (with standard error)

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In