An elaborate data set on human gait and the effect of mechanical perturbations

Abstract

6Here we share a rich gait data set collected from fifteen subjects walking at three speeds on an instrumented treadmill. Each trial consists of 120 seconds of normal walking and 480 seconds of walking while being longitudinally perturbed during each stance phase with pseudo-random fluctuations in the speed of the treadmill belt. A total of approximately 1.5 hours of normal walking (> 5000 gait cycles) and 6 hours of perturbed walking (> 20, 000 gait cycles) is included in the data set. We provide full body marker trajectories and ground reaction loads in addition to a presentation of processed data that includes gait events, 2D joint angles, angular rates, and joint torques along with the open source software used for the computations. The protocol is described in detail and supported with additional elaborate meta data for each trial. This data can likely be useful for validating or generating mathematical models that are capable of simulating normal periodic gait and non-periodic, perturbed gaits.

A controller for walking derived from how humans recover from perturbations

“…One way to examine how humans control walking, or indeed any movement task, is to apply unforeseen perturbations and examine the transients back to steady state. While a number of such walking perturbation experiments have been performed [5][6][7][8][9][10][11][12][13][14][15], none of them have been used to derive a controller sufficient for a complete, even if simplified, simulation of walking. Instead, the perturbation experiments have only been used to obtain insights into limited aspects of walking control such as foot placement or centre of pressure modulation [5,7,[15][16][17][18], ankle impedance [12,19], impulse response functions for some muscles [14,15] or some aspect of body motion [9].…”

Compensation for inertial and gravity effects in a moving force platform

“…To assess the importance of inertial and gravitational artifacts and the ef fectiveness of the compensation, we used human gait data that was collected on a stationary instrumented treadmill where the artifacts were negligible (Moore et al, 2015). A standard sagittal plane inverse dynamic analysis was performed (Winter, 1990)…”

Peer Review #3 of "An elaborate data set on human gait and the effect of mechanical perturbations (v0.1)"

“…We developed a toolkit for data processing, GaitAnalysisToolKit v0.1.2 (Moore et al, 2014b) for common gait computations and provide an example processed trial to present the nature of the data. The tool was developed in Python, is dependent on the SciPy Stack [NumPy (Walt et al, 2011), SciPy (Jones et al, 2001), matplotlib (Hunter, 2007), Pandas (McKinney, 2010, etc] and Octave (Octave community, 2014), and provides two main classes: one to do basic gait data cleaning from D-Flow's output files, DFlowData, and a second to compute common gait variables of interest, GaitData.…”

“…The data set (Moore et al, 2014a) is available via the Zenodo data repository. Two approximately 1.2GB gzipped tar balls contain the data and a README file with a short description of the contents.…”