CROSS RECURRENCE QUANTIFICATION ANALYSIS OF INTER-LEG RELATIONS ACROSS THE GAIT TRANSITION

The aim of this study was to quantify characteristics of the deterministic properties of inter-leg movements over the gait transition. The purpose was to further understanding of the nonlinear characteristics of the gait of a healthy individual. A participant locomoted on a treadmill as the speed steadily increased from 0 to 18 km/h over a 120 s period. Position of the approximate center of rotation of the bilateral toe, ankle, knee and hip, were collected (CODAmotion; 100 Hz). The mathematical addition of the four markers representing the “free leg” in the direction of travel (x direction) provided the variables “left leg” and “right leg”. Each leg was embedded with a dimension of 3 and time lag of 18 points to create the Taken’s vector. Cross Recurrent Quantification Analysis (RQA) was then performed. Two distinct phases in the combined dynamics of the leg-leg system were observed, with lower determinism in the walking phase compared to running, separated by a clear, sharp transition. Results indicate that determinism in the cross dynamics of the two legs seems to play a role similar to an order parameter for the walk-run phase transition. This work increases our understanding of the nonlinear dynamics characteristics of the gait transition

INFLUENCE OF CALCULATION PARAMETERS ON NONLINEAR DYNAMICS MEASURES

Nonlinear analysis methods based on a dynamical systems approach have become more prevalent in recent biomechanics studies. The aim of this study is to identify the range of time delay and embedding dimension values estimated for gait data, and how sensitive Lyapunov exponent and correlation dimension are to the range of these values. A participant walked at 3 km/h and ran at 9 km/h on a treadmill for 2 minutes. Lyaponuv exponent and correlation dimension were calculated based on a combination of the optimal and mean average time delay (TD) and embedding dimension (ED) for both angle and marker data. In the majority of literature, only one ED and TD is considered for LyE estimation, based on an average across data or values suggested in previous papers. However, the results of our study show that every angle or position, has an optimum ED and TD, and the use of these values affects the nonlinear dynamics values in non-trivial ways