Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders.

Rana Zia Ur Rehman,Lisa Alcock,Tibor Hortobágyi,Claudine J C Lamoth,Yu Guan,Clint Hansen,Yuhan Zhou,Lynn Rochester,Silvia Del Din,Walter Maetzler

doi:10.3390/s20236992

Abstract

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43–99% sensitivity and 48–98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Highlights

The world population is getting older and the risk of falling increases with age [1]
Model performance was evaluated with commonly used evaluation metrics such as the F1 score, area under the curve (AUC), accuracy, sensitivity and specificity to avoid any misinterpretation of the machine learning (ML) results
The findings from this study suggest that faller classification models trained on gait characteristics pre-processed with the path signature method may be generalised across patient groups with mobility problems

Summary

Introduction

The world population is getting older and the risk of falling increases with age [1]. One third of adults over 65 experience at least one fall each year [2] and this proportion increases with age [3]. It is crucial to identify people with neurological disorders at risk of falls, before a fall occurs, so that interventions are offered early. Extrinsic (e.g., weather, lighting, uneven surfaces) and intrinsic (e.g., cognition, vision, muscle strength, gait) factors can predispose individuals to falls [8,9,10]. The strongest independent intrinsic fall risk factors are physical weakness, gait and balance impairments, psychoactive medications and previous falls [12,13]. Gait speed is considered as a marker of global health, and by evaluating gait using instrumented assessments, it is possible to assess individual fall risk [17,18]

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