Gait and Dynamic Balance Sensing Using Wearable Foot Sensors

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Abstract

Remote monitoring of gait performance offers possibilities for objective evaluation, and tackling impairment in motor ability, gait, and balance in populations such as elderly, stroke, multiple sclerosis, Parkinson’s, etc. This requires a wearable and unobtrusive system capable of estimating ambulatory gait and balance measures, such as Extrapolated Centre of Mass (XCoM) and dynamic Margin of Stability (MoS). These estimations require knowledge of 3D forces and moments (F&M), and accurate foot positions. Though an existing Ambulatory Gait and Balance System (AGBS) consisting of 3D F&M sensors, and inertial measurement units (IMUs) can be used for the purpose, it is bulky and conspicuous. Resistive pressure sensors were investigated as an alternative to the onboard 3D F&M sensors. Subject specific regression models were built to estimate 3D F&M from 1D plantar pressures. The model was applicable for different walking speeds. Different pressure sensor configurations were studied to optimise system complexity and accuracy. Using resistive sensors only under the toe and heel, we were able to estimate the XCoM with a mean absolute RMS error of 2.20.3 cm in the walking direction while walking at a preferred speed, when compared to the AGBS. For the same case, the XCoM was classified as ahead or behind the Base of Support correctly at 97.7 1.7%. In conclusion, the study shows that pressure sensors, minimally under the heel and toe, offer a lightweight and inconspicuous alternative for F&M sensing, towards estimating ambulatory gait and dynamic balance.
Original languageEnglish
Pages (from-to)218-227
Number of pages10
JournalIEEE transactions on neural systems and rehabilitation engineering
Volume27
Issue number2
Early online date26 Dec 2018
DOIs
Publication statusPublished - Feb 2019

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Pressure sensors
Gait
Foot
Sensors
Pressure
Heel
Toes
Monitoring
Multiple Sclerosis
Walking
Stroke
Population

Cite this

@article{f470ba44a3a54ebb8a96d15fddafba96,
title = "Gait and Dynamic Balance Sensing Using Wearable Foot Sensors",
abstract = "Remote monitoring of gait performance offers possibilities for objective evaluation, and tackling impairment in motor ability, gait, and balance in populations such as elderly, stroke, multiple sclerosis, Parkinson’s, etc. This requires a wearable and unobtrusive system capable of estimating ambulatory gait and balance measures, such as Extrapolated Centre of Mass (XCoM) and dynamic Margin of Stability (MoS). These estimations require knowledge of 3D forces and moments (F&M), and accurate foot positions. Though an existing Ambulatory Gait and Balance System (AGBS) consisting of 3D F&M sensors, and inertial measurement units (IMUs) can be used for the purpose, it is bulky and conspicuous. Resistive pressure sensors were investigated as an alternative to the onboard 3D F&M sensors. Subject specific regression models were built to estimate 3D F&M from 1D plantar pressures. The model was applicable for different walking speeds. Different pressure sensor configurations were studied to optimise system complexity and accuracy. Using resistive sensors only under the toe and heel, we were able to estimate the XCoM with a mean absolute RMS error of 2.20.3 cm in the walking direction while walking at a preferred speed, when compared to the AGBS. For the same case, the XCoM was classified as ahead or behind the Base of Support correctly at 97.7 1.7{\%}. In conclusion, the study shows that pressure sensors, minimally under the heel and toe, offer a lightweight and inconspicuous alternative for F&M sensing, towards estimating ambulatory gait and dynamic balance.",
author = "{Mohamed Refai}, {Mohamed Irfan} and {van Beijnum}, {Bernhard J.F.} and Buurke, {Jaap Hilbert} and Veltink, {Petrus H.}",
year = "2019",
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language = "English",
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pages = "218--227",
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AU - Mohamed Refai, Mohamed Irfan

AU - van Beijnum, Bernhard J.F.

AU - Buurke, Jaap Hilbert

AU - Veltink, Petrus H.

PY - 2019/2

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N2 - Remote monitoring of gait performance offers possibilities for objective evaluation, and tackling impairment in motor ability, gait, and balance in populations such as elderly, stroke, multiple sclerosis, Parkinson’s, etc. This requires a wearable and unobtrusive system capable of estimating ambulatory gait and balance measures, such as Extrapolated Centre of Mass (XCoM) and dynamic Margin of Stability (MoS). These estimations require knowledge of 3D forces and moments (F&M), and accurate foot positions. Though an existing Ambulatory Gait and Balance System (AGBS) consisting of 3D F&M sensors, and inertial measurement units (IMUs) can be used for the purpose, it is bulky and conspicuous. Resistive pressure sensors were investigated as an alternative to the onboard 3D F&M sensors. Subject specific regression models were built to estimate 3D F&M from 1D plantar pressures. The model was applicable for different walking speeds. Different pressure sensor configurations were studied to optimise system complexity and accuracy. Using resistive sensors only under the toe and heel, we were able to estimate the XCoM with a mean absolute RMS error of 2.20.3 cm in the walking direction while walking at a preferred speed, when compared to the AGBS. For the same case, the XCoM was classified as ahead or behind the Base of Support correctly at 97.7 1.7%. In conclusion, the study shows that pressure sensors, minimally under the heel and toe, offer a lightweight and inconspicuous alternative for F&M sensing, towards estimating ambulatory gait and dynamic balance.

AB - Remote monitoring of gait performance offers possibilities for objective evaluation, and tackling impairment in motor ability, gait, and balance in populations such as elderly, stroke, multiple sclerosis, Parkinson’s, etc. This requires a wearable and unobtrusive system capable of estimating ambulatory gait and balance measures, such as Extrapolated Centre of Mass (XCoM) and dynamic Margin of Stability (MoS). These estimations require knowledge of 3D forces and moments (F&M), and accurate foot positions. Though an existing Ambulatory Gait and Balance System (AGBS) consisting of 3D F&M sensors, and inertial measurement units (IMUs) can be used for the purpose, it is bulky and conspicuous. Resistive pressure sensors were investigated as an alternative to the onboard 3D F&M sensors. Subject specific regression models were built to estimate 3D F&M from 1D plantar pressures. The model was applicable for different walking speeds. Different pressure sensor configurations were studied to optimise system complexity and accuracy. Using resistive sensors only under the toe and heel, we were able to estimate the XCoM with a mean absolute RMS error of 2.20.3 cm in the walking direction while walking at a preferred speed, when compared to the AGBS. For the same case, the XCoM was classified as ahead or behind the Base of Support correctly at 97.7 1.7%. In conclusion, the study shows that pressure sensors, minimally under the heel and toe, offer a lightweight and inconspicuous alternative for F&M sensing, towards estimating ambulatory gait and dynamic balance.

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