Mechanics of very slow human walking

Journal Article (2019)
Author(s)

Amy R. Wu (Queen’s University)

Cole S. Simpson (Stanford University)

Edwin van Asseldonk (University of Twente)

H. van der Kooij (University of Twente, TU Delft - Biomechatronics & Human-Machine Control)

Auke Ijspeert (École Polytechnique Fédérale de Lausanne)

Research Group
Biomechatronics & Human-Machine Control
Copyright
© 2019 Amy R. Wu, Cole S. Simpson, Edwin H.F. van Asseldonk, H. van der Kooij, Auke J. Ijspeert
DOI related publication
https://doi.org/10.1038/s41598-019-54271-2
More Info
expand_more
Publication Year
2019
Language
English
Copyright
© 2019 Amy R. Wu, Cole S. Simpson, Edwin H.F. van Asseldonk, H. van der Kooij, Auke J. Ijspeert
Research Group
Biomechatronics & Human-Machine Control
Issue number
1
Volume number
9
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Human walking speeds can be influenced by multiple factors, from energetic considerations to the time to reach a destination. Neurological deficits or lower-limb injuries can lead to slower walking speeds, and the recovery of able-bodied gait speed and behavior from impaired gait is considered an important rehabilitation goal. Because gait studies are typically performed at faster speeds, little normative data exists for very slow speeds (less than 0.6 ms− 1). The purpose of our study was to investigate healthy gait mechanics at extremely slow walking speeds. We recorded kinematic and kinetic data from eight adult subjects walking at four slow speeds from 0.1 ms− 1 to 0.6 ms− 1 and at their self-selected speed. We found that known relations for spatiotemporal and work measures are still valid at very slow speeds. Trends derived from slow speeds largely provided reasonable estimates of gait measures at self-selected speeds. Our study helps enable valuable comparisons between able-bodied and impaired gait, including which pathological behaviors can be attributed to slow speeds and which to gait deficits. We also provide a slow walking dataset, which may serve as normative data for clinical evaluations and gait rehabilitative devices.