Skip to main content

Nonlinear Analysis Core

The Nonlinear Analysis Core provides resources and services necessary for innovative analysis of human movement. These methods go beyond averages by looking at the time-varying characteristics of a time signal. The Core provides access to a multitude of nonlinear analysis tools, assistance in experimental design, data processing, quality assurance, interpretation and dissemination. The Core is also actively exploring and validating new techniques and algorithms for future use. In addition to our nonlinear methods, standard analyses can also be performed.

Resources

The Core currently has expertise in the following methods.

nonlinear-chart.jpg

Annual Nonlinear Analysis Workshop

Every year the Core holds a workshop centered on the nonlinear analysis of human movement. Its purpose is to introduce research scientists, clinicians, educators and students to a variety of nonlinear methods. For more information please visit the Workshop’s webpage.

Online Webinars

The Core offers online webinars over different nonlinear analysis topics.

Rates

The current rate is $50 per hour.

How to Initiate Work with the Core

  1. Complete a Project Initiation Form and send it to bmchnonan@unomaha.edu.
  2. This will be followed by a communication from the Core to better understand your needs.
  3. The Core will send a contract regarding the project.
  4. The Core will send a quote for the work.
  5. Provide to the Core data or other items needed to complete the work.
  6. Work commences.

Personnel

  • Dr. Jenna Yentes, Core Director
  • Mr. Benjamin Senderling, Core Coordinator
  • Mr. Cory Frederick, Core Programmer

Citations

Here is a selection of recent publications. For more please see the Center’s publication page.

  • Almurad, Z. M., & Delignières, D. Evenly spacing in detrended fluctuation analysis. Physica A: Statistical Mechanics and its Applications, 451, 63-69, 2016.
  • Bollens, B., Crevecoeur, F., Nguyen, V., Detrembleur, C., & Lejeune, T. Does human gait exhibit comparable and reproducible long-range autocorrelations on level ground and on treadmill?. Gait & posture, 32(3), 369-373, 2010.
  • Damouras, S., Chang, M. D., Sejdić, E., & Chau, T. An empirical examination of detrended fluctuation analysis for gait data. Gait & posture, 31(3), 336-340, 2010.
  • Liddy, J. J., Ducharme, S. W., van Emmerik, R. E., & Haddad, J. M. Temporal correlations in human locomotion: Recommendations for sampling rate and foot strike detection. Physica A: Statistical Mechanics and its Applications, 532, 121784, 2019.
  • Liddy, J. J., & Haddad, J. M. Evenly spaced Detrended Fluctuation Analysis: Selecting the number of points for the diffusion plot. Physica A: Statistical Mechanics and its Applications, 491, 233-248, 2018.
  • McCamley J, Denton W, Arnold A, Raffalt PC, Yentes JM. On the calculation of sample entropy using continuous versus discrete data sets. Entropy, 20(10):764, 2018. PMC6402504.
  • McCamley J, Denton W, Lyden E, Yentes JM. Measuring coupling of rhythmical time series using cross sample entropy and cross recurrence quantification analysis. Computational and Mathematical Methods in Medicine, 2017, 7960467, 2017. PMC5671691.
  • Raffalt PC, Denton W, Yentes JM. On the choice of multiscale entropy algorithm of complexity in gait data. Computers in Biology and Medicine, 103:93-100, 2018. PMC6957257.
  • Raffalt PC, McCamley J, Denton W, Yentes JM. Sampling frequency influences sample entropy of kinematics during walking. Medical & Biological Engineering and Computing, 57(4):759-764, 2019. PMC6450768.
  • Raffalt PC, Yentes JM. Introducing statistical persistence decay – A quantification of stride-to-stride time interval dependency in human gait. Annals of Biomedical Engineering, 46(1):60-70, 2018. PMC5756114.
  • Roume, C., Ezzina, S., Blain, H., & Delignières, D. Biases in the Simulation and Analysis of Fractal Processes. Computational and Mathematical Methods in Medicine, 2019.
  • Terrier, P., & Reynard, F. Maximum Lyapunov exponent revisited: long-term attractor divergence of gait dynamics is highly sensitive to the noise structure of stride intervals. Gait & posture, 66, 236-241, 2018.
  • Warlop, T. B., Bollens, B., Detrembleur, C., Stoquart, G., Lejeune, T., & Crevecoeur, F. Impact of series length on statistical precision and sensitivity of autocorrelation assessment in human locomotion. Human movement science, 55, 31-42, 2017.
  • Yentes JM, Denton W, McCamley J, Raffalt PC, Schmid KK. Effect of parameter selection on entropy calculation for long walking trials. Gait & Posture, 60:128-124, 2018. PMC5809187.
  • Yentes JM, Hunt N, Schmid KK, Kaipust JP, McGrath D, Stergiou N. The appropriate use of approximate entropy and sample entropy with short data sets. Annals of Biomedical Engineering, 41(2):349-365, 2013.  PMC6549512.
  • Yentes JM, Raffalt PC. On the application of entropic half-life and statistical persistence decay for quantification of time dependency in human gait. (Accepted with Journal of Biomechanics).