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

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.

Source: Physical Medicine and Rehabilitation Clinics of North America - March 10, 2019 Category: Rehabilitation Authors: Antonio Prado, Xiya Cao, Maxime T. Robert, Andrew K. Gordon, Sunil K. Agrawal Source Type: research