The influence of gait speed on local dynamic stability of walking

Abstract

The focus of this study was to examine the role of walking velocity in stability during normal gait. Local dynamic stability was quantified through the use of maximum finite-time Lyapunov exponents, λ Max . These quantify the rate of attenuation of kinematic variability of joint angle data recorded as subjects walked on a motorized treadmill at 20%, 40%, 60%, and 80% of the Froude velocity. A monotonic trend between λ Max and walking velocity was observed with smaller λ Max at slower walking velocities. Smaller λ Max indicates more stable walking dynamics. This trend was evident whether stride duration variability remained or was removed by time normalizing the data. This suggests that slower walking velocities lead to increases in stability. These results may reveal more detailed information on the behavior of the neuro-controller than variability-based analyses alone.

Kinematic measures for assessing gait stability in elderly individuals: a systematic review

“…This approach, however, is not without controversy. For example, England & Granata [100] and Kang & Dingwell [101] found increased LEs at increasing walking speeds indicating decreased dynamic stability at higher speeds. On the other hand, Bruijn et al [87] using LEs at different walking speeds (in young cohorts) found both increasing and decreasing stability values at a higher speed depending on the estimates used (l_s versus l_l) as well as the projected directions anteroposterior (AP) versus mediolateral (ML).…”

Gait dynamics, fractals and falls: Finding meaning in the stride-to-stride fluctuations of human walking

“…Before beginning, it is important to note that because of space constraints, this review considers only a subset of the reports on this topic. For complementary perspectives, the reader is referred to other pertinent reviews (Chau, 2001a(Chau, , 2001bChau, Young, & Redekop, 2005;Hausdorff, 2005) as well as parallel studies by others (Buzzi, Stergiou, Kurz, Hageman, & Heidel, 2003;Costa, Peng, Goldberger, & Hausdorff, 2003;Dingwell, Cusumano, Cavanagh, & Sternad, 2001;Dingwell, Gu Kang, & Marin, 2006;England & Granata, 2007;Georgoulis, Moraiti, Ristanis, & Stergiou, 2006;Granata & England, 2006;Kurz et al, 2003;Kurz & Stergiou, 2005;Stergiou, Harbourne, & Cavanaugh, 2006). …”

Toward ambulatory balance assessment: Estimating variability and stability from short bouts of gait

“…In addition, the number of embedding dimensions used in this study, i.e. nine, was higher than in many other studies [8,12,16,20] but is still within the range of five to twelve used in the literature [8,10,[12][13][14]16,18,20,24,30]. This broad range of embedding dimensions might be due to the use of different types of data, such as electromyography, joint angles, angular velocity or linear accelerations, and the embedding of one single signal or more; and might call for standardization of state space reconstruction in gait research.…”

“…The data series length was normalized to on average 99 samples per stride to eliminate the effect of varying data series length on the local dynamic stability estimates [16,20]. Subsequently, a state space was reconstructed using all three accelerations and their time-delayed copies [21,22].…”

“…the smallest detectable difference (SDD), was estimated. These smallest detectable differences were expressed in percentages of the mean to maintain comparability with other studies since methodological choices such as state space reconstruction or data series length have a large influence on mean values of local dynamic stability [20,21,24].…”