Gait Segmentation of Data Collected by Instrumented Shoes Using a Recurrent Neural Network Classifier.

TitleGait Segmentation of Data Collected by Instrumented Shoes Using a Recurrent Neural Network Classifier.
Publication TypeJournal Article
Year of Publication2019
AuteursPrado, A, Cao, X, Robert, MT, Gordon, AM, Agrawal, SK
JournalPhys Med Rehabil Clin N Am
Volume30
Issue2
Pagination355-366
Date Published2019 05
ISSN1558-1381
KeywordsAdolescent, Adult, Cerebral Palsy, Child, Equipment Design, Female, Gait Analysis, Humans, Male, Neural Networks, Computer, Robotics, Shoes, Young Adult
Abstract

The authors present a Recurrent Neural Network classifier model that segments the walking data recorded with instrumented footwear. The signals from 3 piezoresistive sensors, a 3-axis accelerometer, and Euler angles are used to generate temporal gait characteristics of a user. The model was tested using a data set collected from 28 adults containing 4198 steps. The mean errors for heel strikes and toe-offs were -5.9 ± 37.1 and 11.4 ± 47.4 milliseconds. These small errors show that the algorithm can be reliably used to segment the gait recordings and to use this segmentation to estimate temporal parameters of the subjects.

DOI10.1016/j.pmr.2018.12.007
Alternate JournalPhys Med Rehabil Clin N Am
PubMed ID30954152