Recurrent Neural Network for Gait Pathology Detection

Jorge Sanchez-Casanova, Judith Liu-Jimenez, Pablo Fernandez-Lopez, Raul Sanchez-Reillo

2020

Abstract

This work presents a pathology detection system on the lower train. For this, a database of healthy subjects has been captured. Due to the nonexistence of pathological gait databases, pathology walks have been simulated. The users used sole padding in order to simulate clubfoot walk. The database consists of acceleration, angular acceleration, magnetic field signals and the angles between the joints. The algorithm extracts fragments of the signals which are used to train a recurrent neural network (RNN). To optimize the results, hand-tuning method was used to modify the hyperparameters. Using the best configuration, we have a 97% accuracy training with 90% of the database. Although, if we train with only 50% of the data the accuracy reaches at 91%. The results obtained show the solution feasibility, although further research should be done using real lower train pathologies.

Paper Citation

in Harvard Style

Sanchez-Casanova J., Liu-Jimenez J., Fernandez-Lopez P. and Sanchez-Reillo R. (2020). Recurrent Neural Network for Gait Pathology Detection. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS; ISBN 978-989-758-398-8, SciTePress, pages 60-67. DOI: 10.5220/0008910600600067

in Bibtex Style

@conference{biosignals20,
author={Jorge Sanchez-Casanova and Judith Liu-Jimenez and Pablo Fernandez-Lopez and Raul Sanchez-Reillo},
title={Recurrent Neural Network for Gait Pathology Detection},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS},
year={2020},
pages={60-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008910600600067},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS
TI - Recurrent Neural Network for Gait Pathology Detection
SN - 978-989-758-398-8
AU - Sanchez-Casanova J.
AU - Liu-Jimenez J.
AU - Fernandez-Lopez P.
AU - Sanchez-Reillo R.
PY - 2020
SP - 60
EP - 67
DO - 10.5220/0008910600600067
PB - SciTePress