Multiscale entropy analysis of human gait dynamics

M. Costa; C. K. Peng; Ary L. Goldberger; Jeffrey M. Hausdorff

doi:10.1016/j.physa.2003.08.022

Multiscale entropy analysis of human gait dynamics

M. Costa
, C. K. Peng
, Ary L. Goldberger
, Jeffrey M. Hausdorff^*

^*Corresponding author for this work

Harvard University
University of Lisbon
Tel Aviv Sourasky Medical Center

Research output: Contribution to journal › Conference article › peer-review

468 Scopus citations

Abstract

We compare the complexity of human gait time series from healthy subjects under different conditions. Using the recently developed multiscale entropy algorithm, which provides a way to measure complexity over a range of scales, we observe that normal spontaneous walking has the highest complexity when compared to slow and fast walking and also to walking paced by a metronome. These findings have implications for modeling locomotor control and for quantifying gait dynamics in physiologic and pathologic states.

Original language	English
Pages (from-to)	53-60
Number of pages	8
Journal	Physica A: Statistical Mechanics and its Applications
Volume	330
Issue number	1-2
DOIs	https://doi.org/10.1016/j.physa.2003.08.022
State	Published - 1 Dec 2003
Externally published	Yes
Event	Randomes and Complexity - Eilat, Israel Duration: 5 Jan 2003 → 9 Jan 2003

Funding

Funders	Funder number
National Institutes of Health	RR-13622, AG-08812, HD-39838, AG-14100
G. Harold and Leila Y. Mathers Charitable Foundation

Keywords

Complexity
Human gait
Locomotion
Multiscale entropy
Neural control

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10.1016/j.physa.2003.08.022

Cite this

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title = "Multiscale entropy analysis of human gait dynamics",

abstract = "We compare the complexity of human gait time series from healthy subjects under different conditions. Using the recently developed multiscale entropy algorithm, which provides a way to measure complexity over a range of scales, we observe that normal spontaneous walking has the highest complexity when compared to slow and fast walking and also to walking paced by a metronome. These findings have implications for modeling locomotor control and for quantifying gait dynamics in physiologic and pathologic states.",

keywords = "Complexity, Human gait, Locomotion, Multiscale entropy, Neural control",

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note = "Funding Information: This work was supported in part by NIH grants AG-14100, RR-13622, HD-39838 and AG-08812, and by the G. Harold and Leila Y. Mathers Charitable Foundation. ; Randomes and Complexity ; Conference date: 05-01-2003 Through 09-01-2003",

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AU - Costa, M.

AU - Peng, C. K.

AU - Goldberger, Ary L.

AU - Hausdorff, Jeffrey M.

N1 - Funding Information: This work was supported in part by NIH grants AG-14100, RR-13622, HD-39838 and AG-08812, and by the G. Harold and Leila Y. Mathers Charitable Foundation.

PY - 2003/12/1

Y1 - 2003/12/1

N2 - We compare the complexity of human gait time series from healthy subjects under different conditions. Using the recently developed multiscale entropy algorithm, which provides a way to measure complexity over a range of scales, we observe that normal spontaneous walking has the highest complexity when compared to slow and fast walking and also to walking paced by a metronome. These findings have implications for modeling locomotor control and for quantifying gait dynamics in physiologic and pathologic states.

AB - We compare the complexity of human gait time series from healthy subjects under different conditions. Using the recently developed multiscale entropy algorithm, which provides a way to measure complexity over a range of scales, we observe that normal spontaneous walking has the highest complexity when compared to slow and fast walking and also to walking paced by a metronome. These findings have implications for modeling locomotor control and for quantifying gait dynamics in physiologic and pathologic states.

KW - Complexity

KW - Human gait

KW - Locomotion

KW - Multiscale entropy

KW - Neural control

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JO - Physica A: Statistical Mechanics and its Applications

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IS - 1-2

T2 - Randomes and Complexity

Y2 - 5 January 2003 through 9 January 2003

ER -

Multiscale entropy analysis of human gait dynamics

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