Human gait monitoring using body-worn inertial sensors and kinematic modelling

Authors: Amin Ahmadi François Destelle David Monaghan Kieran Moran Noel E. OConnor María Teresa Linaza Saldaña Luis Unzueta Irurtia

Date: 01.11.2015


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Abstract

In this paper, we present a low-cost computationally efficient method to accurately assess Gait by monitoring the 3D trajectory of the lower limb (i.e. 3 segments - foot, tibia and thigh, and 2 joints - ankle and knee). Our method utilises a network of miniaturized wireless inertial sensors, coupled with a suite of sophisticated real-time analysis algorithms and can operate in any unconstrained environment. Firstly, we adopt a modified computationally-efficient, highly accurate and realtime gradient descent algorithm to obtain the 3D orientation of each of the 3 segments. Secondly, by utilising the foot sensor, we successfully detect the stance phase of the human gait cycle, which allows us to obtain drift-free velocity and the 3D position of the foot during functional phases of a gait cycle (i.e. heel strike to heel strike). Thirdly, by setting the foot segment as the root node we calculate the 3D orientation and position of the other 2 segments as well as the ankle and knee joints. Finally, we employ a customised kinematic model adjustment technique to ensure that the motion is coherent with human biomechanical behaviour of the leg. Our method is low-cost, is robust to measurement drift and can accurately monitor human gait outside the lab in any unconstrained environment.

BIB_text

@Article {
title = {Human gait monitoring using body-worn inertial sensors and kinematic modelling},
pages = {1-4},
keywds = {

Motion capture, gait analysis, inertial sensors, inverse kinematics


}
abstract = {

In this paper, we present a low-cost computationally efficient method to accurately assess Gait by monitoring the 3D trajectory of the lower limb (i.e. 3 segments - foot, tibia and thigh, and 2 joints - ankle and knee). Our method utilises a network of miniaturized wireless inertial sensors, coupled with a suite of sophisticated real-time analysis algorithms and can operate in any unconstrained environment. Firstly, we adopt a modified computationally-efficient, highly accurate and realtime gradient descent algorithm to obtain the 3D orientation of each of the 3 segments. Secondly, by utilising the foot sensor, we successfully detect the stance phase of the human gait cycle, which allows us to obtain drift-free velocity and the 3D position of the foot during functional phases of a gait cycle (i.e. heel strike to heel strike). Thirdly, by setting the foot segment as the root node we calculate the 3D orientation and position of the other 2 segments as well as the ankle and knee joints. Finally, we employ a customised kinematic model adjustment technique to ensure that the motion is coherent with human biomechanical behaviour of the leg. Our method is low-cost, is robust to measurement drift and can accurately monitor human gait outside the lab in any unconstrained environment.


}
isbn = {978-1-4799-8202-8},
date = {2015-11-01},
year = {2015},
}
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