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Gait Posture. 2019 Feb;68:280-284. doi: 10.1016/j.gaitpost.2018.12.006. Epub 2018 Dec 5.

A prediction method of speed-dependent walking patterns for healthy individuals.

Author information

1
Federal University of ABC, Neuroscience and Biomedical Engineering Programs, São Bernardo do Campo, São Paulo, Brazil.
2
Federal University of ABC, Neuroscience and Biomedical Engineering Programs, São Bernardo do Campo, São Paulo, Brazil. Electronic address: duartexyz@gmail.com.

Abstract

BACKGROUND:

Gait speed is one of the main biomechanical determinants of human movement patterns. However, in clinical gait analysis, the effect of gait speed is generally not considered, and people with disabilities are usually compared with able-bodied individuals even though disabled people tend to walk slower.

RESEARCH QUESTIONS:

This study proposes a simple way to predict the gait pattern of healthy individuals at a specific speed.

METHODS:

The method consists of creating a reference database for a range of gait speeds, and the gait-pattern prediction is implemented as follows: 1) the gait cycle is discretized from 0 to 100% for each variable, 2) a first or second-order polynomial is used to adjust the values of the reference dataset versus the corresponding gait speeds for each instant of the gait cycle to obtain the parameters of the regression, and 3) these regression parameters are then used to predict the new values of the gait pattern at any specific speed. Twenty-four healthy adults walked on the treadmill at eight different gait speeds, where the gait pattern was obtained by a 3D motion capture system and an instrumented treadmill.

RESULTS:

Overall, the predicted data presented good agreement with the experimental data for the joint angles and joint moments.

SIGNIFICANCE:

These results demonstrated that the proposed prediction method can be used to generate more unbiased reference data for clinical gait analysis and might be suitably applied to other speed-dependent human movement patterns.

KEYWORDS:

Gait analysis; Kinematics; Kinetics; Prediction methods; Regression analysis; Walking

PMID:
30551054
DOI:
10.1016/j.gaitpost.2018.12.006
[Indexed for MEDLINE]

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