An artificial neural network framework for lower limb motion signal estimation with foot-mounted inertial sensors
Author(s)
Sun, Y
Yang, guangzhong
Lo, benny
Type
Conference Paper
Abstract
This paper proposes a novel artificial neural
network based method for real-time gait analysis with minimal
number of Inertial Measurement Units (IMUs). Accurate lower
limb attitude estimation has great potential for clinical gait di-
agnosis for orthopaedic patients and patients with neurological
diseases. However, the use of multiple wearable sensors hinder
the ubiquitous use of inertial sensors for detailed gait analysis.
This paper proposes the use of two IMUs mounted on the
shoes to estimate the IMU signals at the shin, thigh and waist
for accurate attitude estimation of the lower limbs. By using
the artificial neural network framework, the gait parameters,
such as angle, velocity and displacements of the IMUs can
be estimated. The experimental results have shown that the
proposed method can accurately estimate the IMUs signals on
the lower limbs based only on the IMU signals on the shoes,
which demonstrates its potential for lower limb motion tracking
and real-time gait analysis.
network based method for real-time gait analysis with minimal
number of Inertial Measurement Units (IMUs). Accurate lower
limb attitude estimation has great potential for clinical gait di-
agnosis for orthopaedic patients and patients with neurological
diseases. However, the use of multiple wearable sensors hinder
the ubiquitous use of inertial sensors for detailed gait analysis.
This paper proposes the use of two IMUs mounted on the
shoes to estimate the IMU signals at the shin, thigh and waist
for accurate attitude estimation of the lower limbs. By using
the artificial neural network framework, the gait parameters,
such as angle, velocity and displacements of the IMUs can
be estimated. The experimental results have shown that the
proposed method can accurately estimate the IMUs signals on
the lower limbs based only on the IMU signals on the shoes,
which demonstrates its potential for lower limb motion tracking
and real-time gait analysis.
Date Issued
2018-03-04
Date Acceptance
2017-12-18
Publisher
IEEE
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
British Council (UK)
Grant Number
EP/K503733/1
EP/L014149/1
540213 SeNTH plus
330760239
Source
IEEE Conference on Body Sensor Networks (BSN) 2018
Publication Status
Accepted
Start Date
2018-03-04
Finish Date
2018-03-07
Coverage Spatial
Las Vegas, USA