Citation

Porta S, Martinez A, Millor N, Gomez M, Izquierdo M. J. Biomech. 2020; ePub(ePub): ePub.

Affiliation

Navarrabiomed, Complejo Hospitalario de Navarra (CHN)-Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain; CIBER of Frailty and Healthy Aging (CIBERFES), Madrid, Spain. Electronic address: mikel.izquierdo@unavarra.es.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.jbiomech.2020.109723

PMID

32151381

Abstract

Approximately one-third of elderly people fall each year with severe consequences, including death. The aim of this study was to identify the most relevant features to be considered to maximize the accuracy of a logistic regression model designed for prediction of fall/mortality risk among older people. This study included 261 adults, aged over 65 years. Men and women were analyzed separately because sex stratification was revealed as being essential for our purposes of feature ranking and selection. Participants completed a 3-m walk test at their own gait velocity. An inertial sensor attached to their lumbar spine was used to record acceleration data in the three spatial directions. Signal processing techniques allowed the extraction of 21 features representative of gait kinematics, to be used as predictors to train and test the model. Age and gait speed data were also considered as predictors. A set of 23 features was considered. These features demonstrate to be more or less relevant depending on the sex of the cohort under analysis and the classification label (risk of falls and mortality). In each case, the minimum size subset of relevant features is provided to show the maximum accuracy prediction capability. Gait speed has been largely used as the single feature for the prediction fall risk among older adults. Nevertheless, prediction accuracy can be substantially improved, reaching 70% in some cases, if the task of training and testing the model takes into account some other features, namely, sex, age and gait kinematic parameters. Therefore we recommend considering sex, age and step regularity to predict fall-risk.

Copyright © 2020 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Feature selection for maximum accuracy prediction; Logistic regression model; Prediction of falls/mortality risk; Sex stratification importance