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

Journal: Physical medicine and rehabilitation clinics of North America
Published Date: Mar 11, 2019

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.

Authors

  • Antonio Prado
    Mechanical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA.
  • Xiya Cao
    Mechanical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA.
  • Maxime T Robert
    Department of Biobehavioral Sciences, Teachers College, Columbia University, 525 West 120th Street, Box 93, New York, NY 10027, USA.
  • Andrew M Gordon
  • Sunil K Agrawal

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