@inproceedings{4d035c5f1fc64bb2964655f4b522b15e,
title = "Gait phase detection optimization based on variational bayesian inference of feedback sensor signal",
abstract = "Stroke patients often suffer from gait disorders which can remain chronic. Mechanical or electrical aids designed to deal with this problem often rely on accurate estimation of current gait phase as this information is used for active ankle joint control. In this paper we present the method for optimization of the gait phase detection algorithm. The method is based on Variational Bayesian inference which is employed on signals from feedback sensors positioned on both paretic and healthy foot of patient. Main aim of Variational Bayesian inference application was to remove noise and provide smooth sensor signal which is suitable for robust gait phase detection algorithm. We modeled foot trajectory with linear model. Results presented in this paper show significant reduction of high frequency noise in gyroscope signal. The reduction was dominant during transitions between gait phases making our method applicable in any algorithm based on signal features in time domain.",
keywords = "Bayesian inference, drop foot, FES, gait kinematics, variational",
author = "Neboj{\v s}a Male{\v s}evi{\'c} and Jovana Male{\v s}evi{\'c} and Thierry Keller",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 ; Conference date: 25-11-2014 Through 27-11-2014",
year = "2014",
month = jan,
day = "15",
doi = "10.1109/NEUREL.2014.7011499",
language = "English",
series = "12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "179--182",
editor = "Branimir Reljin and Srdan Stankovic",
booktitle = "12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 - Proceedings",
address = "United States",
}
