Decrease in Hurst exponent of human gait with aging and neurodegenerative diseases
Decrease in Hurst exponent of human gait with aging and neurodegenerative diseases
Jian-Jun, Zhuang; Xin-Bao, Ning; Xiao-Dong, Yang; Feng-Zhen, Hou; Cheng-Yu, Huo
2008-03-01 00:00:00
In this paper the decrease in the Hurst exponent of human gait with aging and neurodegenerative diseases was observed by using an improved rescaled range (R/S) analysis method. It indicates that the long-range correlations of gait rhythm from young healthy people are stronger than those from the healthy elderly and the diseased. The result further implies that fractal dynamics in human gait will be altered due to weakening or impairment of neural control on locomotion resulting from aging and neurodegenerative diseases. Due to analysing short-term data sequences rather than long datasets required by most nonlinear methods, the algorithm has the characteristics of simplicity and sensitivity, most importantly, fast calculation as well as powerful anti-noise capacities. These findings have implications for modelling locomotor control and also for quantifying gait dynamics in varying physiologic and pathologic states.
http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.pngChinese Physics BIOP Publishinghttp://www.deepdyve.com/lp/iop-publishing/decrease-in-hurst-exponent-of-human-gait-with-aging-and-C35ktYMIIv
Decrease in Hurst exponent of human gait with aging and neurodegenerative diseases
In this paper the decrease in the Hurst exponent of human gait with aging and neurodegenerative diseases was observed by using an improved rescaled range (R/S) analysis method. It indicates that the long-range correlations of gait rhythm from young healthy people are stronger than those from the healthy elderly and the diseased. The result further implies that fractal dynamics in human gait will be altered due to weakening or impairment of neural control on locomotion resulting from aging and neurodegenerative diseases. Due to analysing short-term data sequences rather than long datasets required by most nonlinear methods, the algorithm has the characteristics of simplicity and sensitivity, most importantly, fast calculation as well as powerful anti-noise capacities. These findings have implications for modelling locomotor control and also for quantifying gait dynamics in varying physiologic and pathologic states.
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