Freezing-of-Gait Detection Using Wearable-Sensor Technology and Neural-Network Classifier

Freezing of gait (FoG) is a syndrome of Parkinson's Disease (PD), which causes patients to be unable to initiate and/or maintain the rhythmical and alternating leg movements of locomotion. Since FoG negatively impacts patient quality of life and is a main reason for PD patients to fall, the abi...

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Bibliographic Details
Published in2019 IEEE SENSORS pp. 1 - 4
Main Authors Tahafchi, Parisa, Judy, Jack W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
Freezing of Gait
Neural Network
Wearable Sensors
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Summary:Freezing of gait (FoG) is a syndrome of Parkinson's Disease (PD), which causes patients to be unable to initiate and/or maintain the rhythmical and alternating leg movements of locomotion. Since FoG negatively impacts patient quality of life and is a main reason for PD patients to fall, the ability to quantify FoG episodes with reliable and accurate algorithms could help enable high-value therapies to be developed. In this study, a wearable sensor platform is used to collect inertial and physiological signals to cover all the variations of freezing patterns. A diverse set of features is also introduced and fed to a Fully-Connected-Neural Network (FCNN) to learn and predict freezing episodes.
ISSN:2168-9229
DOI:10.1109/SENSORS43011.2019.8956548