Gait Segmentation of Data Collected by Instrumented Shoes Using a Recurrent Neural Network Classifier.
Phys Med Rehabil Clin N Am
; 30(2): 355-366, 2019 05.
Article
em En
| MEDLINE
| ID: mdl-30954152
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.
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MEDLINE
Assunto principal:
Sapatos
/
Redes Neurais de Computação
/
Análise da Marcha
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article