Relationship between stride interval variability and aging: use of linear and non-linear estimators for gait variability assessment in assisted living environments

Original Research
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Abstract

The aim of this study is to assess the impact of aging process in gait variability. Stride interval variability is estimated by using two approaches: (1) a non-linear fractal analysis (detrended fluctuation analysis) which evaluates the presence of long-range correlations in stride interval time series; and (2) a statistical dispersion measure (coefficient of variation) to quantify the magnitude of the stride interval fluctuations. Two groups of physically independent older adults, with different walking ability, have participated in the gait trials performed in an elderly care home. The estimated stride interval variability of the elders is compared to each other and to the variability coming from a young adult group used as control. To accomplish this, an infrastructure which uses wearables to acquire inertial data from the trunk of each participant is provided. Stride interval time series are made up of the estimated heel-strike events from previous inertial data. In addition, a service segments straight paths within the gait trials, discarding turns. The stride intervals from the segmented straight paths are stitched together to produce the long time series required to analyze gait variability. Despite the study has not provided conclusive results from an individual perspective, finding older adults who have less stride interval variability than younger ones. The inter-class analysis conducted has shown interesting findings about the relation between the subjective characterization of gait, aging and stride interval variability estimated through the two proposed approaches.

Keywords

Quantitative gait analysis Gait variability Aging Assisted living environments Stride interval variability Detrended fluctuation analysis 

Notes

Acknowledgements

This work is supported by the FRASE MINECO project (TIN2013-47152-C3-1-R) and the Plan Propio de Investigación program from Castilla-La Mancha University.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.University of Castilla-La ManchaCiudad RealSpain

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