Key result
Multi-scale entropy (MSE) appears to be the most sensitive algorithm to changes in gait in healthy subjects, whereas approximate entropy (ApEn) is considered less appropriate due to conceptual errors.
Multi-scale entropy (MSE) and sample entropy (SampEn) are more appropriate and sensitive algorithms than approximate entropy (ApEn) for analyzing human gait complexity.
Human gait is a non-linear complex process requiring appropriate mathematical measuring tools. Entropy is a measure that quantifies regularity in time series: the more predictable a series is the lower the entropy value. The mathematical methods used to estimate entropy have evolved over time. At present, three algorithms are the most used to study human gait complexity: the approximate entropy (ApEn), the sample entropy (SampEn), and the multi-scale entropy (MSE). Most studies on human gait complexity have been conducted on elderly subjects or subjects with specific disorders affecting gait patterns and they used ApEn; but, because of a set of conceptual errors, the ApEn is not the most appropriate algorithm for the analysis of biological signals. Very few studies use SampEn or MSE to analyze human gait variability, but they agree that these algorithms might contribute new perspectives in the analysis of human gait and that MSE seems to be the most sensitive algorithm to changes in gait in healthy subjects.
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Bartolomé J. Almagro (2013) conducted a review in Human gait complexity. Entropy algorithms (ApEn, SampEn, MSE) was evaluated. Multi-scale entropy (MSE) appears to be the most sensitive algorithm to changes in gait in healthy subjects, whereas approximate entropy (ApEn) is considered less appropriate due to conceptual errors.
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