Deep learning prediction of gait based on inertial measurements

  1. Pedro Romero-Hernandez
  2. Javier de Lope Asiain
  3. Manuel Graña
Libro:
Understanding the Brain Function and Emotions: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019 Almería, Spain, June 3–7, 2019 Proceedings, Part I
  1. José Manuel Ferrández Vicente (dir. congr.)
  2. José Ramón Álvarez-Sánchez (dir. congr.)
  3. Félix de la Paz López (dir. congr.)
  4. Javier Toledo Moreo (dir. congr.)
  5. Hojjat Adeli (dir. congr.)

Editorial: Springer Suiza

ISBN: 978-3-030-19591-5

Año de publicación: 2019

Páginas: 284-290

Tipo: Capítulo de Libro

DIALNET GOOGLE SCHOLAR

Resumen

We report the application of recurrent deep learning networks, namely long term short memories (LSTM) for the modeling ofgait synchronization of legs using a basic configuration of off-the-shelf inertial measurement units (IMU) providing six acceleration and rotation parameters. The proposed system copes with noisy and missing data due to high sampling rate, before applying the training of LSTM. We report accurate testing results on one experimental subject. This model can be transferred to robotised prostheses and assistive robotics devices in order to achieve quick stabilization and robust transfer of control algorithms to new users.

Fuente de los datos: Dialnet