Determinants of gait stability while walking on a treadmill: a machine learning approach

TL;DR

This study investigates how individual characteristics like age and preferred walking speed influence gait stability in healthy adults using machine learning, revealing key factors and interactions affecting dynamic balance during treadmill walking.

Contribution

It applies machine learning algorithms to identify the influence of age and walking speed on gait stability, highlighting the importance of these factors in LDS assessments.

Findings

Preferred walking speed and age significantly influence gait LDS.

No significant effects of sex, height, or body mass on LDS.

An interaction between age and PWS affects gait stability.

Abstract

Dynamic balance in human locomotion can be assessed through the local dynamic stability (LDS) method. Whereas gait LDS has been used successfully in many settings and applications, little is known about its sensitivity to individual characteristics of healthy adults. Therefore, we reanalyzed a large dataset of accelerometric data measured for 100 healthy adults from 20 to 70 years of age performing 10 min. treadmill walking. We sought to assess the extent to which the variations of age, body mass and height, sex, and preferred walking speed (PWS) could influence gait LDS. The random forest (RF) and multiple adaptive regression splines (MARS) algorithms were selected for their good bias-variance tradeoff and their capabilities to handle nonlinear associations. First, through variable importance measure (VIM), we used RF to evaluate which individual characteristics had the highest influence on gait LDS. Second, we used MARS to detect potential interactions among individual characteristics that may influence LDS. The VIM and MARS results indicated that PWS and age correlated with LDS, whereas no associations were found for sex, body height, and body mass. Further, the MARS model detected an age by PWS interaction: on one hand, at high PWS, gait stability is constant across age while, on the other hand, at low PWS, gait instability increases substantially with age. We conclude that it is advisable to consider the participants' age as well as their PWS to avoid potential biases in evaluating dynamic balance through LDS.