Development of Linear Regression Models to Estimate the Margin of Stability Based on Spatio-Temporal Gait Parameters

Rami Alamoudi,Mohammed Alamoudi

doi:10.1109/access.2020.2969294

Abstract

Spatio-temporal gait parameters such as step width, cadence, stride length, and walking speed contribute to dynamic stability. Several studies have investigated the role of gait parameters in maintaining balance. However, in these studies, subjects were instructed to alter their gait. This intentional alteration has the potential to create error in the results, as subjects are not walking with a natural and comfortable gait. In consideration of this, the sample chosen in this study consisted of patients who had undergone a knee replacement. Such individuals naturally have gait parameters that differ from normal subjects. The primary objective of this study was to develop regression models that predict and measure gait stability in both the anterior-posterior and medio-lateral directions based on gait parameters. The maximum deviation of the extrapolated center of mass from the border of the base of support was the measure of gait stability. A forward stepwise multiple regression analysis was conducted to develop both models. In testing the goodness of fit of models, the values of coefficient of determination, standard error of estimates, and root mean square error were calculated. Both models showed sufficient values of goodness of fit. To improve walking stability and minimize falls, fall-prone people should walk with an adequate base-of-support area, and with lower cadence and speed. The results of this study contribute to an understanding of gait patterns and their relationship to walking stability and to how gait strategies might be taught in physical therapy programs to minimize the risk of falls.

Highlights

Dynamic stability is required for the successful performance of a variety of activities of daily living
Control of locomotive stability is considered a complicated task since it needs the coordination of nervous, sensory, and motor systems to control the center of mass (CoM) in an accurate manner
The objective of this study was to develop regression models that predict A/P and M/L gait stability based on gait parameters such as walking speed, cadence, step width, and stride length

Summary

Introduction

Dynamic stability is required for the successful performance of a variety of activities of daily living. Gait instability is associated with a higher risk of falling [1]. One out of five fall incidents causes a serious injury, such as head injuries or broken bones [3], [4]. 3 million elderly people are treated for fall injuries, with at least 300,000 people being hospitalized for hip fractures [5], [6]. More than 95% of hip injuries are caused by falling, usually by sideways falling [7], [8]. Falls are the most common cause of traumatic brain injuries [9]. Some of the most important factors that contribute to a fall are difficulties in walking and maintaining stability

Objectives

Methods

Results

Conclusion