ArticleJournal of biomechanics2016
Characterizing and modeling the joint-level variability in human walking.
Article in Journal of biomechanics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Quantifying the effect of sagittal plane joint angle variability on bipedal fall risk.PloS one · 2022Article
- Variable Impedance Control of Powered Knee Prostheses Using Human-Inspired Algebraic Curves.Journal of computational and nonlinear dynamics · 2019Article
- Stable, Robust Hybrid Zero Dynamics Control of Powered Lower-Limb Prostheses.IEEE transactions on automatic control · 2017Article
- A Robust Parameterization of Human Gait Patterns Across Phase-Shifting Perturbations.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2017Article
- Incorporating Human-like Walking Variability in an HZD-Based Bipedal Model.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Although human gait is often assumed to be periodic, significant variability exists. This variability appears to provide different information than the underlying periodic signal, particularly about fall risk. Most studies on variability have either used step-to-step metrics such as stride duration or point-wise standard deviations, neither of which explicitly capture the joint-level variability as a function of time. This work demonstrates that a second-order Fourier series for stance joints and a first-order Fourier series for swing joints can accurately capture the variability in joint angles as a function of time on a per-step basis for overground walking at the self-selected speed. It further demonstrates that a total of seven normal distributions, four linear relationships, and twelve continuity constraints can be used to describe how the Fourier series vary between steps. The ability of the proposed method to create curves that match human joint-level variability was evaluated both qualitatively and quantitatively using randomly generated curves.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.