Imperial College London
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DrBennyLo

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Visiting Reader
 
 
 
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Contact

 

+44 (0)20 7594 0806benny.lo Website

 
 
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Location

 

Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Sun:2018,
author = {Sun, Y and Yang, G and Lo, B},
publisher = {IEEE},
title = {An artificial neural network framework for lower limb motion signal estimation with foot-mounted inertial sensors},
url = {http://hdl.handle.net/10044/1/57658},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - This   paper   proposes   a   novel   artificial   neuralnetwork based method for real-time gait analysis with minimalnumber of Inertial Measurement Units (IMUs). Accurate lowerlimb attitude estimation has great potential for clinical gait di-agnosis for orthopaedic patients and patients with neurologicaldiseases. However, the use of multiple wearable sensors hinderthe ubiquitous use of inertial sensors for detailed gait analysis.This  paper  proposes  the  use  of  two  IMUs  mounted  on  theshoes to estimate the IMU signals at the shin, thigh and waistfor  accurate  attitude  estimation  of  the  lower  limbs.  By  usingthe  artificial  neural  network  framework,  the  gait  parameters,such  as  angle,  velocity  and  displacements  of  the  IMUs  canbe  estimated.  The  experimental  results  have  shown  that  theproposed method can accurately estimate the IMUs signals onthe  lower  limbs  based  only  on  the  IMU  signals  on  the  shoes,which demonstrates its potential for lower limb motion trackingand  real-time  gait  analysis.
AU  - Sun,Y
AU  - Yang,G
AU  - Lo,B
PB  - IEEE
PY  - 2018///
TI  - An artificial neural network framework for lower limb motion signal estimation with foot-mounted inertial sensors
UR  - http://hdl.handle.net/10044/1/57658
ER  -
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