Horst Fabian, Eekhoff Alexander, Newell Karl M, Schöllhorn Wolfgang I
Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Mainz, Rhineland-Palatinate, Germany.
Department of Kinesiology, University of Georgia, Athens, Georgia, United States of America.
PLoS One. 2017 Jun 15;12(6):e0179738. doi: 10.1371/journal.pone.0179738. eCollection 2017.
Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours).
Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns.
Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales.
Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of these findings, especially towards more individualized and situational diagnoses and therapy.
传统上，步态分析一直围绕平均行为和正常性的概念展开。一方面，临床诊断和治疗干预通常假定平均步态模式随时间保持不变。另一方面，众所周知，我们所有的动作都伴随着一定程度的变异性，这使得我们无法迈出完全相同的两步。本研究的目的是检查个体内部步态模式在不同时间尺度（即几十分钟、几十小时）上的变化。
9名健康受试者在一天内分6次以自选速度进行15次步态试验（两次连续试验之间的持续时间为10至90分钟）。对于每次试验，测量时间连续的地面反作用力和下肢关节角度。应用基于核的判别回归的监督学习模型对个体步态模式内的试验进行分类。
通过对地面反作用力和下肢关节角度进行六次试验分类，个体内部步态模式的可辨别特征在重复试验之间得以区分，分类率分别为67.8±8.8%和86.3±7.9%。此外，一对一分类表明，分类率的提高与两次试验之间时间间隔的增加相关，这表明步态模式的变化出现在不同的时间尺度上。
重复试验之间的可辨别特征表明个体内部步态模式存在持续的内在变化，并表明确定性过程在人类运动控制和学习中起主要作用。在没有任何外部诱发损伤或干预的情况下，步态模式的自然变化可能反映了运动系统在多个时间尺度上的持续适应。因此，鉴于这些发现，需要重新考虑通过假定随时间近乎恒定的平均步态模式来对行走进行建模，尤其是在更个性化和情境化的诊断与治疗方面。
