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Intelligent Systems and ApplicationsA Deep Learning-Based Approach for the Classification of Gait Dynamics in Subjects with a Neurodegenerative Disease

Intelligent Systems and Applications: A Deep Learning-Based Approach for the Classification of... [Neurodegenerative diseases cause changes in neuromuscular tissues through a deterioration of the motor neurons, which make the motor capability of a patient increasingly abnormal. In particular, walking is one of the movements most significantly influenced by the deterioration process. An early detection of emerging anomalies in the walking patterns of elderly subjects may help to prevent connected risks. The current walking patterns assessment methods are generally performed in supervised clinical environments and show limitations in terms of cost and accuracy. In this work, we aim to provide a contribution to the analysis of walking patterns so we address the problem of the recognition of gait dynamics by the exploration of the application of deep learning algorithms. In order to prove the goodness of our work, we have carried out five experiments, each with a different classification task. The results achieve a classification accuracy which is better by 3.9% than the accuracy achieved by models presented in related works.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Intelligent Systems and ApplicationsA Deep Learning-Based Approach for the Classification of Gait Dynamics in Subjects with a Neurodegenerative Disease

Part of the Advances in Intelligent Systems and Computing Book Series (volume 1252)
Editors: Arai, Kohei; Kapoor, Supriya; Bhatia, Rahul
Paragliola, Giovanni; Coronato, Antonio
Intelligent Systems and Applications — Aug 25, 2020
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References (29)

Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2021
ISBN
978-3-030-55189-6
Pages
452–468
DOI
10.1007/978-3-030-55190-2_34
Publisher site
See Chapter on Publisher Site

Abstract

[Neurodegenerative diseases cause changes in neuromuscular tissues through a deterioration of the motor neurons, which make the motor capability of a patient increasingly abnormal. In particular, walking is one of the movements most significantly influenced by the deterioration process. An early detection of emerging anomalies in the walking patterns of elderly subjects may help to prevent connected risks. The current walking patterns assessment methods are generally performed in supervised clinical environments and show limitations in terms of cost and accuracy. In this work, we aim to provide a contribution to the analysis of walking patterns so we address the problem of the recognition of gait dynamics by the exploration of the application of deep learning algorithms. In order to prove the goodness of our work, we have carried out five experiments, each with a different classification task. The results achieve a classification accuracy which is better by 3.9% than the accuracy achieved by models presented in related works.]

Published: Aug 25, 2020

Keywords: Deep learning; Convolutional neural network; LSTM; Human behaviors recognition; Gait classification; Neurodegenerative diseases; Deep neural network

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