Quaternions and joint angles in an analysis of local stability of gait for different variants of walking speed and treadmill slope

The authors describe an example of application of a nonlinear time series analysis directed at identifying the presence of deterministic chaos in human gait kinematic data by means of the largest Lyapunov exponent (LLE). A positive LLE value is interpreted as an indicator of local instability. The research was aimed at assessment of the influence of both walking speed and ground slope on the resilience of gait control to infinitesimally small perturbations that occur naturally during walking. The analysis of treadmill gait data was carried out twofold: 1) for the time series representing the following joint angles: hip flexion/extension, knee flexion/extension and dorsiflexion/plantarflexion of the ankle, and 2) for the time series representing rotations of foot, tibia and femur segments through Euler angles converted to a quaternion representation. A comparison between both approaches as well as a dependency between treadmill inclination and LLE values constitute the original aspects of this study. The LLE value was estimated threefold for every time series: as the short-term LLE for both the first step and the first stride and as the long-term LLE over a fixed interval between the fourth and the tenth stride. It was confirmed that all considered movements are characterized by positive LLE values which quantify a local instability. Moreover, a tendency to attenuate the perturbation consequences is evident in all variants of walking speed and treadmill slope.

[1]  H J Dananberg,et al.  Sagittal plane biomechanics. American Diabetes Association. , 2000, Journal of the American Podiatric Medical Association.

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

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

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

[5]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[6]  A. Achiron,et al.  The effect of balance training on postural control in people with multiple sclerosis using the CAREN virtual reality system: a pilot randomized controlled trial , 2016, Journal of NeuroEngineering and Rehabilitation.

[7]  Maxime Raison,et al.  Agreement of spatio-temporal gait parameters between a vertical ground reaction force decomposition algorithm and a motion capture system. , 2016, Gait & posture.

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

[9]  Yueting Zhuang,et al.  Exploiting temporal stability and low-rank structure for motion capture data refinement , 2014, Inf. Sci..

[10]  Peter J Beek,et al.  Stride frequency and length adjustment in post-stroke individuals: influence on the margins of stability. , 2015, Journal of rehabilitation medicine.

[11]  Ching-Kun Chen,et al.  Individual identification based on chaotic electrocardiogram signals , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.

[12]  O. Meijer,et al.  Phase-dependent changes in local dynamic stability during walking in elderly with and without knee osteoarthritis. , 2016, Journal of biomechanics.

[13]  M. Perc The dynamics of human gait , 2005 .

[14]  P. Beek,et al.  Maximum Lyapunov exponents as predictors of global gait stability: a modelling approach. , 2012, Medical engineering & physics.

[15]  Jie Zhao,et al.  Applications of Chaotic Dynamics in Robotics , 2016 .

[16]  W. Taylor,et al.  Quantifying spinal gait kinematics using an enhanced optical motion capture approach in adolescent idiopathic scoliosis. , 2016, Gait & posture.

[17]  C Basdogan,et al.  Kinematics and dynamic stability of the locomotion of post-polio patients. , 1996, Journal of biomechanical engineering.

[18]  Alessio Gizzi,et al.  Effects of Pacing Site and Stimulation History on Alternans Dynamics and the Development of Complex Spatiotemporal Patterns in Cardiac Tissue , 2013, Front. Physiol..

[19]  Metin Akay,et al.  Wiley encyclopedia of biomedical engineering , 2006 .

[20]  Michal Piórek,et al.  Chaotic Properties of Gait Kinematic Data , 2015, CISIM.

[21]  N. Stergiou,et al.  The effect of the walking speed on the stability of the anterior cruciate ligament deficient knee. , 2004, Clinical biomechanics.

[22]  Stanislaw Osowski,et al.  Epileptic seizure characterization by Lyapunov exponent of EEG signal , 2007 .

[23]  H. Kantz A robust method to estimate the maximal Lyapunov exponent of a time series , 1994 .

[24]  N. Stergiou,et al.  A Novel Approach to Measure Variability in the Anterior Cruciate Ligament Deficient Knee During Walking: The Use of the Approximate Entropy in Orthopaedics , 2006, Journal of Clinical Monitoring and Computing.

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

[26]  D. Sternad,et al.  Local dynamic stability versus kinematic variability of continuous overground and treadmill walking. , 2001, Journal of biomechanical engineering.

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

[28]  J. Dingwell,et al.  Dynamic stability of individuals with transtibial amputation walking in destabilizing environments. , 2014, Journal of biomechanics.

[29]  P. Beek,et al.  Assessing the stability of human locomotion: a review of current measures , 2013, Journal of The Royal Society Interface.

[30]  M. Arif,et al.  Estimation of the Effect of Cadence on Gait Stability in Young and Elderly People using Approximate Entropy Technique , 2004 .

[31]  Guoqing Zhang,et al.  Human motion recovery jointly utilizing statistical and kinematic information , 2016, Inf. Sci..

[32]  Yunfeng Wu,et al.  Statistical Analysis of Gait Rhythm in Patients With Parkinson's Disease , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  Metin Akay,et al.  Nonlinear Dynamics Time Series Analysis , 2000 .

[34]  James A. Norris,et al.  Revisiting the stability of 2D passive biped walking: Local behavior , 2008 .

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

[36]  F. Mormann,et al.  Seizure prediction: the long and winding road. , 2007, Brain : a journal of neurology.

[37]  Beatrix Vereijken,et al.  Phase-dependent changes in local dynamic stability of human gait. , 2012, Journal of biomechanics.

[38]  F. Takens Detecting strange attractors in turbulence , 1981 .

[39]  Philippe Terrier,et al.  Do orthopaedic shoes improve local dynamic stability of gait? An observational study in patients with chronic foot and ankle injuries , 2013, BMC Musculoskeletal Disorders.

[40]  Rodger Kram,et al.  Dynamic stability of running: the effects of speed and leg amputations on the maximal Lyapunov exponent. , 2013, Chaos.

[41]  P. Terrier,et al.  Non-linear dynamics of human locomotion: effects of rhythmic auditory cueing on local dynamic stability , 2012, Front. Physiol..

[42]  Jonathan B Dingwell,et al.  Dynamic margins of stability during human walking in destabilizing environments. , 2012, Journal of biomechanics.

[43]  D. Kugiumtzis State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length , 1996, comp-gas/9602002.

[44]  T. Kaminski,et al.  Neuromuscular Control and Ankle Instability , 2009, PM & R : the journal of injury, function, and rehabilitation.