Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74444
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Title: Multivariate multiscale symbolic entropy analysis of human gait signals
Authors: Yu, J
Cao, J
Liao, WH
Chen, Y
Lin, J
Liu, R 
Issue Date: 2017
Source: Entropy, 2017, v. 19, no. 10, 557, p. 2
Abstract: The complexity quantification of human gait time series has received considerable interest for wearable healthcare. Symbolic entropy is one of the most prevalent algorithms used to measure the complexity of a time series, but it fails to account for the multiple time scales and multi-channel statistical dependence inherent in such time series. To overcome this problem, multivariate multiscale symbolic entropy is proposed in this paper to distinguish the complexity of human gait signals in health and disease. The embedding dimension, time delay and quantization levels are appropriately designed to construct similarity of signals for calculating complexity of human gait. The proposed method can accurately detect healthy and pathologic group from realistic multivariate human gait time series on multiple scales. It strongly supports wearable healthcare with simplicity, robustness, and fast computation.
Keywords: Complexity
Entropy
Human gait
Multivariate multiscale symbolic entropy
Symbolic entropy
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Entropy 
ISSN: 1099-4300
EISSN: 1099-4300
DOI: 10.3390/e19100557
Rights: © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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