Understanding ageing effects using complexity analysis of foot–ground clearance during walking

Ageing influences gait patterns which in turn can affect the balance control of human locomotion. Entropy-based regularity and complexity measures have been highly effective in analysing a broad range of physiological signals. Minimum toe clearance (MTC) is an event during the swing phase of the gait cycle and is highly sensitive to the spatial balance control properties of the locomotor system. The aim of this research was to investigate the regularity and complexity of the MTC time series due to healthy ageing and locomotors' disorders. MTC data from 30 healthy young (HY), 27 healthy elderly (HE) and 10 falls risk (FR) elderly subjects with balance problems were analysed. Continuous MTC data were collected and using the first 500 data points, MTC mean, standard deviation (SD) and entropy-based complexity analysis were performed using sample entropy (SampEn) for different window lengths (m) and filtering levels (r). The MTC SampEn values were lower in the FR group compared to the HY and HE groups for all m and r. The HY group had a greater mean SampEn value than both HE and FR reflecting higher complexity in their MTC series. The mean SampEn values of HY and FR groups were found significantly different for m = 2, 4, 5 and r = (0.1–0.9) × SD, (0.3–0.9) × SD and (0.3–0.9) × SD, respectively. They were also significant difference between HE and FR groups for m = 4–5 and r = (0.3–0.7) × SD, but no significant differences were seen between HY and HE groups for any m and r. A significant correlation of SampEn with SD of MTC was revealed for the HY and HE groups only, suggesting that locomotor disorders could significantly change the regularity or the complexity of the MTC series while healthy ageing does not. These results can be usefully applied to the early diagnosis of common gait pathologies.

[1]  M. Palaniswami,et al.  Investigating Scale Invariant Dynamics in Minimum Toe Clearance Variability of the Young and Elderly During Treadmill Walking , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  S. Studenski,et al.  Stance time and step width variability have unique contributing impairments in older persons. , 2008, Gait & posture.

[3]  M. Palaniswami,et al.  A comparative study on approximate entropy measure and poincaré plot indexes of minimum foot clearance variability in the elderly during walking , 2008, Journal of NeuroEngineering and Rehabilitation.

[4]  Daniel T. Schmitt,et al.  Fractal scale-invariant and nonlinear properties of cardiac dynamics remain stable with advanced age: a new mechanistic picture of cardiac control in healthy elderly. , 2007, American journal of physiology. Regulatory, integrative and comparative physiology.

[5]  M. Palaniswami,et al.  Understanding Ageing Effects by Approximate Entropy Analysis of gait variability , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  K. Newell,et al.  Walking speed influences on gait cycle variability. , 2007, Gait & posture.

[7]  R. Begg,et al.  Minimum foot clearance during walking: strategies for the minimisation of trip-related falls. , 2007, Gait & posture.

[8]  Scott A. England,et al.  The influence of gait speed on local dynamic stability of walking. , 2007, Gait & posture.

[9]  Jeffrey M. Hausdorff Gait variability: methods, modeling and meaning , 2005, Journal of NeuroEngineering and Rehabilitation.

[10]  Marimuthu Palaniswami,et al.  Support vector machines for automated gait classification , 2005, IEEE Transactions on Biomedical Engineering.

[11]  Nir Giladi,et al.  Gait instability and fractal dynamics of older adults with a "cautious" gait: why do certain older adults walk fearfully? , 2005, Gait & posture.

[12]  Nir Giladi,et al.  Impaired regulation of stride variability in Parkinson's disease subjects with freezing of gait , 2003, Experimental Brain Research.

[13]  K. Newell,et al.  Changing complexity in human behavior and physiology through aging and disease , 2002, Neurobiology of Aging.

[14]  Karl M Newell,et al.  Complexity in aging and disease: response to commentaries , 2002, Neurobiology of Aging.

[15]  Jeffrey M. Hausdorff,et al.  Fractal dynamics in physiology: Alterations with disease and aging , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Jeffrey M. Hausdorff,et al.  Gait variability and fall risk in community-living older adults: a 1-year prospective study. , 2001, Archives of physical medicine and rehabilitation.

[17]  E Glucksman,et al.  Falls in the elderly: what can be done? , 2000, The Medical journal of Australia.

[18]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[19]  Jeffrey M. Hausdorff,et al.  Gait variability and basal ganglia disorders: Stride‐to‐stride variations of gait cycle timing in parkinson's disease and Huntington's disease , 1998, Movement disorders : official journal of the Movement Disorder Society.

[20]  B. E. Maki,et al.  Gait Changes in Older Adults: Predictors of Falls or Indicators of Fear? , 1997, Journal of the American Geriatrics Society.

[21]  Jeffrey M. Hausdorff,et al.  Increased gait unsteadiness in community-dwelling elderly fallers. , 1997, Archives of physical medicine and rehabilitation.

[22]  L. Liebovitch,et al.  "Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations". , 1996, Journal of applied physiology.

[23]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[24]  C. Marsden,et al.  Human walking and higher‐level gait disorders, particularly in the elderly , 1993, Neurology.

[25]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[26]  Steven M. Pincus,et al.  Quantification of hormone pulsatility via an approximate entropy algorithm. , 1992, The American journal of physiology.

[27]  Steven M. Pincus,et al.  A regularity statistic for medical data analysis , 1991, Journal of Clinical Monitoring.

[28]  P. Grassberger,et al.  NONLINEAR TIME SEQUENCE ANALYSIS , 1991 .

[29]  D. T. Kaplan,et al.  Aging and the complexity of cardiovascular dynamics. , 1991, Biophysical journal.

[30]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[31]  P. Grassberger Finite sample corrections to entropy and dimension estimates , 1988 .

[32]  U. Nayak,et al.  The effect of age on variability in gait. , 1984, Journal of gerontology.

[33]  P. Grassberger,et al.  Estimation of the Kolmogorov entropy from a chaotic signal , 1983 .

[34]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[35]  F. Borg,et al.  Entropy of balance - some recent results , 2010, Journal of NeuroEngineering and Rehabilitation.

[36]  J. Dingwell,et al.  Kinematic variability and local dynamic stability of upper body motions when walking at different speeds. , 2006, Journal of biomechanics.

[37]  T. Hermana,et al.  Gait instability and fractal dynamics of older adults with a “ cautious ” gait : why do certain older adults walk fearfully ? , 2005 .

[38]  Jeffrey M. Hausdorff,et al.  Multiscale entropy analysis of human gait dynamics. , 2003, Physica A.

[39]  Jeffrey M. Hausdorff,et al.  Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. , 1995, Journal of applied physiology.

[40]  Steven M. Pincus,et al.  Approximate entropy: Statistical properties and applications , 1992 .

[41]  R. Arking Biology of Aging: Observations and Principles , 1991 .

[42]  M. Rockstein,et al.  Biology of aging , 1979 .