Imperial College London

DrBennyLo

Faculty of MedicineDepartment of Surgery & Cancer

Senior Lecturer
 
 
 
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Contact

 

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

 
 
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Location

 

B414BBessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Sun,
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},
}

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
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 -