Next Article in Journal
How News May Affect Markets’ Complex Structure: The Case of Cambridge Analytica
Next Article in Special Issue
Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics
Previous Article in Journal
Entropic Steering Criteria: Applications to Bipartite and Tripartite Systems
Previous Article in Special Issue
Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle

On the Calculation of Sample Entropy Using Continuous and Discrete Human Gait Data

1
MORE Foundation, 18444 N 25th Ave., Suite 110, Phoenix, AZ 85023, USA
2
Center for Research in Human Movement Variability, Department of Biomechanics, University of Nebraska at Omaha, 6160 University Drive South, Omaha, NE 68182-0860, USA
3
Julius Wolff Institute for Biomechanics and Musculoskeletal Regeneration, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
4
Department of Biomedical Sciences, University of Copenhagen, Blegdamsvej 3B, Copenhagen N 2200, Denmark
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(10), 764; https://doi.org/10.3390/e20100764
Received: 1 August 2018 / Revised: 24 September 2018 / Accepted: 26 September 2018 / Published: 5 October 2018
(This article belongs to the Special Issue The 20th Anniversary of Entropy - Approximate and Sample Entropy)
  |  
PDF [39146 KB, uploaded 16 October 2018]
  |  

Abstract

Sample entropy (SE) has relative consistency using biologically-derived, discrete data >500 data points. For certain populations, collecting this quantity is not feasible and continuous data has been used. The effect of using continuous versus discrete data on SE is unknown, nor are the relative effects of sampling rate and input parameters m (comparison vector length) and r (tolerance). Eleven subjects walked for 10-minutes and continuous joint angles (480 Hz) were calculated for each lower-extremity joint. Data were downsampled (240, 120, 60 Hz) and discrete range-of-motion was calculated. SE was quantified for angles and range-of-motion at all sampling rates and multiple combinations of parameters. A differential relationship between joints was observed between range-of-motion and joint angles. Range-of-motion SE showed no difference; whereas, joint angle SE significantly decreased from ankle to knee to hip. To confirm findings from biological data, continuous signals with manipulations to frequency, amplitude, and both were generated and underwent similar analysis to the biological data. In general, changes to m, r, and sampling rate had a greater effect on continuous compared to discrete data. Discrete data was robust to sampling rate and m. It is recommended that different data types not be compared and discrete data be used for SE. View Full-Text
Keywords: range of motion; joint angle; gait; complexity; regularity range of motion; joint angle; gait; complexity; regularity
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

McCamley, J.D.; Denton, W.; Arnold, A.; Raffalt, P.C.; Yentes, J.M. On the Calculation of Sample Entropy Using Continuous and Discrete Human Gait Data. Entropy 2018, 20, 764.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top