A Wireless Sensorized Insole Design for Spatio-Temporal Gait Analysis

Agarwal, Ravinder; Aggarwal, Abhinandan; Gupta, Rohit

doi:10.1007/s11062-020-09873-2

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…In general, the majority of the methodological studies analysing the performance of different pressure insoles, focused on gait parameters other than ICs and FCs and reported larger errors (Agarwal et al, 2020;Braun et al, 2015;Carbonaro et al, 2016;Crea et al, 2014). For instance, the average error reported in Carbonaro et al (2016) by comparing a commercial smart shoe including two force sensors (FootMov) against a motion capture system was 39 ± 65 ms for stance duration.…”

Section: Discussionmentioning

confidence: 99%

A method for gait events detection based on low spatial resolution pressure insoles data

Salis

Bertuletti

Bonci

et al. 2021

Journal of Biomechanics

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The accurate identification of initial and final foot contacts is a crucial prerequisite for obtaining a reliable estimation of spatio-temporal parameters of gait. Well-accepted gold standard techniques in this field are force platforms and instrumented walkways, which provide a direct measure of the foot-ground reaction forces. Nonetheless, these tools are expensive, non-portable and restrict the analysis to laboratory settings. Instrumented insoles with a reduced number of pressure sensing elements might overcome these limitations, but a suitable method for gait events identification has not been adopted yet. The aim of this paper was to present and validate a method aiming at filling such void, as applied to a system including two insoles with 16 pressure sensing elements (element area = 310 mm 2 ), sampling at 100Hz. Gait events were identified exploiting the sensor redundancy and a cluster-based strategy. The method was tested in the laboratory against force platforms on nine healthy subjects for a total of 801 initial and final contacts. Initial and final contacts were detected with low average errors of (about 20 ms and 10 ms, respectively). Similarly, the errors in estimating stance duration and step duration averaged 20 ms and less than 10 ms, respectively. By selecting appropriate thresholds, the method may be easily applied to other pressure insoles featuring similar requirements.

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