Extraction of basic movement from whole-body movement, based on gait variability

Abstract

The aim of this study was to quantify the step-to-step variability (SSV) in speed-variant and speed-invariant movement components of the whole-body gait pattern during running. These separate aspects of variability can be used to gain insight into the neuromuscular control strategies that are engaged during running. Ten healthy, physically active, male recreational athletes performed five treadmill running trials at five different speeds (range: 1.3–4.9 m/sec). The whole-body movement was separated into principal movements (PM) using a principal component analysis. The PMs were split into two groups: a speed-variant group, where the range of motion (amplitude of PMs) changed with running speed; and a speed-invariant group, where the range of motion was constant across various speeds. The step-to-step variability (SSV) of the two groups was then quantified. The absolute SSV was the summed variability across all gait cycles, whereas the relative SSV was the summed variability divided by the magnitude of the movement. The absolute SSV of the speed-variant movements increased with running speed. By contrast, the relative SSV of the speed-variant group (as normalized to the PM amplitude) decreased asymptotically toward a minimal level as running speed increased. Both the absolute and relative SSV of the speed-invariant movements revealed a minimum at 3.1 m/sec. The whole-body gait pattern during running can be subdivided into speed-variant and speed-invariant movements. An interpretation of the SSV based on minimal intervention theory suggests that speed-variant movements are more tightly controlled, as evidenced by a lower degree of variability compared to the speed-invariant movements.

Task Demand Changes Motor Control Strategies in Vertical Jumping

“…In the present study the focus of analysis was on the first three PCs, however there is evidence to suggest intermediate and higher order PCs reveal further differences between conditions or individuals, which would not have been apparent with only an analysis of lower order PCs (Daffertshofer, et al, 2004;Lamoth, Daffertshofer, Meijer, & Beek, 2006;Maurer, von Tscharner, Samsom, Baltich, & Nigg, 2013;Phinyomark et al, 2015). Therefore, future Dimensionality reduction in vertical jumping analyses should seek to determine if higher order PCs when jumping with constraints can reveal further detail into the control process of these tasks.…”

Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions

“…Specifically, the number of variables per subject has increased to ~50–150 discrete variables [ 2 , 3 ], several hundred to thousand variables for joint angle time-series data [ 13 , 14 ], and several thousand to hundred thousand variables for marker coordinate time-series data [ 15 , 16 ]. Although most of these studies continue to involve only a small cohort of subjects (e.g., 10–30 subjects [ 17 , 18 ]) in the analysis, it is generally recommended to expand and further test proposed data science techniques and/or models using datasets with greater subject numbers to determine whether results are similar in different populations. The aforementioned research database can provide the necessary large cohort of subjects for such hypothesis-driven research (e.g., 483 subjects [ 2 ]).…”

“…For example, Barrett and Kline [ 26 ] recommended that the number of subjects should be at least 50 for a PCA approach. Unfortunately, previous research involving a PCA approach have utilized small cohorts of subjects ( n = 10–30) [ 17 , 18 ]. Thus, to minimize the high-dimensionality of the data, big data in terms of big volume (i.e., a large cohort of subjects) are needed to re-evaluate the proposed model and find a consensus among various models or studies.…”

Influence of the Lower Jaw Position on the Running Pattern

“…They have been successfully used to identify small changes of the movement pattern introduced for instance with different footwear [ 41 ] or to characterise the gait pattern of healthy humans [ 42 ]. In order to differentiate random fluctuations of the gait pattern from systematic differences between two conditions the variability of the gait pattern can be used [ 43 ].…”