Measures of gait stability: performance on adults and toddlers at the beginning of independent walking

BackgroundQuantifying gait stability is a topic of high relevance and a number of possible measures have been proposed. The problem in validating these methods is the necessity to identify a-priori unstable individuals. Since proposed methods do not make any assumption on the characteristics of the subjects, the aim of the present study was to test the performance of gait stability measures on individuals whose gait is a-priori assumed unstable: toddlers at the onset of independent walking.MethodsTen toddlers, ten adults and ten elderly subjects were included in the study. Data from toddlers were acquired longitudinally over a 6-month period to test if the methods detected the increase in gait stability with experience, and if they could differentiate between toddlers and young adults. Data from elderly subjects were expected to indicate a stability value in between the other two groups. Accelerations and angular velocities of the trunk and of the leg were measured using two tri-axial inertial sensors. The following methods for quantifying gait stability were applied: stride time variability, Poincaré plots, harmonic ratio, short term Lyapunov exponents, maximum Floquet multipliers, recurrence quantification analysis and multiscale entropy. An unpaired t-test (level of significance of 5%) was performed on the toddlers and the young adults for each method and, for toddlers, for each evaluated stage of gait development.ResultsMethods for discerning between the toddler and the adult groups were: stride time variability, Poincaré plots, harmonic ratio, short term Lyapunov exponents (state space composed by the three linear accelerations of the trunk), recurrence quantification analysis and multiscale entropy (when applied on the vertical or on the antero-posterior L5 accelerations).ConclusionsResults suggested that harmonic ratio and recurrence quantification analysis better discern gait stability in the analyzed subjects, differentiating not only between unstable toddlers and stable healthy adults, but also evidencing the expected trend of the toddlers towards a higher stability with walking experience, and indicating elderly subjects as stable as or less stable than young adults.

[1]  Anil K. Bera,et al.  A test for normality of observations and regression residuals , 1987 .

[2]  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.

[3]  Jane E. Clark,et al.  A longitudinal study of intralimb coordination in the first year of independent walking: a dynamical systems analysis. , 1993, Child development.

[4]  M. Rosenstein,et al.  A practical method for calculating largest Lyapunov exponents from small data sets , 1993 .

[5]  Y. Hurmuzlu,et al.  On the measurement of dynamic stability of human locomotion. , 1994, Journal of biomechanical engineering.

[6]  C L Webber,et al.  Dynamical assessment of physiological systems and states using recurrence plot strategies. , 1994, Journal of applied physiology.

[7]  J. Hamill,et al.  Energetic Cost and Stability During Human Walking at the Preferred Stride Velocity. , 1995, Journal of motor behavior.

[8]  J. Hamill,et al.  Energetic Cost and Stability during Human Walking at the Preferred Stride Frequency , 1995 .

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

[10]  M. Turvey,et al.  Recurrence quantification analysis of postural fluctuations. , 1999, Gait & posture.

[11]  J. Dingwell,et al.  Nonlinear time series analysis of normal and pathological human walking. , 2000, Chaos.

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

[13]  D. Sternad,et al.  Slower speeds in patients with diabetic neuropathy lead to improved local dynamic stability of continuous overground walking. , 2000, Journal of biomechanics.

[14]  Marimuthu Palaniswami,et al.  Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? , 2001, IEEE Transactions on Biomedical Engineering.

[15]  Dan B. Marghitu,et al.  Dynamics of children with torsional anomalies of the lower limb joints , 2001 .

[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]  Kamiar Aminian,et al.  Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. , 2002, Journal of biomechanics.

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

[19]  R. Fitzpatrick,et al.  Acceleration patterns of the head and pelvis when walking are associated with risk of falling in community-dwelling older people. , 2003, The journals of gerontology. Series A, Biological sciences and medical sciences.

[20]  N. Stergiou,et al.  Nonlinear dynamics indicates aging affects variability during gait. , 2003, Clinical biomechanics.

[21]  A L Hof,et al.  The condition for dynamic stability. , 2005, Journal of biomechanics.

[22]  Jonathan B Dingwell,et al.  Differences between local and orbital dynamic stability during human walking. , 2007, Journal of biomechanical engineering.

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

[24]  Beth A. Smith,et al.  Effect of Practice on a Novel Task—Walking on a Treadmill: Preadolescents With and Without Down Syndrome , 2007, Physical Therapy.

[25]  Mark Latt,et al.  Walking speed, cadence and step length are selected to optimize the stability of head and pelvis accelerations , 2007, Experimental Brain Research.

[26]  Fuyuan Liao,et al.  Multi-resolution entropy analysis of gait symmetry in neurological degenerative diseases and amyotrophic lateral sclerosis. , 2008, Medical engineering & physics.

[27]  Thurmon E Lockhart,et al.  Differentiating fall-prone and healthy adults using local dynamic stability , 2008, Ergonomics.

[28]  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.

[29]  P. Beek,et al.  Is slow walking more stable? , 2009, Journal of biomechanics.

[30]  Peter J. Beek,et al.  Statistical precision and sensitivity of measures of dynamic gait stability , 2009, Journal of Neuroscience Methods.

[31]  Beth A. Smith,et al.  Lyapunov exponent and surrogation analysis of patterns of variability: profiles in new walkers with and without down syndrome. , 2010, Motor control.

[32]  Jaap H van Dieën,et al.  Sensitivity of trunk variability and stability measures to balance impairments induced by galvanic vestibular stimulation during gait. , 2011, Gait & posture.

[33]  Navrag B. Singh,et al.  Kinematic measures for assessing gait stability in elderly individuals: a systematic review , 2011, Journal of The Royal Society Interface.

[34]  Jesse M. Lingeman,et al.  How Do You Learn to Walk? Thousands of Steps and Dozens of Falls per Day , 2012, Psychological science.

[35]  Jaap H van Dieën,et al.  Local dynamic stability and variability of gait are associated with fall history in elderly subjects. , 2012, Gait & posture.

[36]  F. S. Labini,et al.  Recurrence quantification analysis of gait in normal and hypovestibular subjects. , 2012, Gait & posture.

[37]  R. Stagni,et al.  Orbital stability analysis in biomechanics: a systematic review of a nonlinear technique to detect instability of motor tasks. , 2013, Gait & posture.

[38]  Maria Cristina Bisi,et al.  Gait variability and stability measures: Minimum number of strides and within-session reliability , 2014, Comput. Biol. Medicine.