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2017 | OriginalPaper | Chapter

Deep Learning for Detecting Freezing of Gait Episodes in Parkinson’s Disease Based on Accelerometers

Authors : Julià Camps, Albert Samà, Mario Martín, Daniel Rodríguez-Martín, Carlos Pérez-López, Sheila Alcaine, Berta Mestre, Anna Prats, M. Cruz Crespo, Joan Cabestany, Àngels Bayés, Andreu Català

Published in: Advances in Computational Intelligence

Publisher: Springer International Publishing

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Abstract

Freezing of gait (FOG) is one of the most incapacitating symptoms among the motor alterations of Parkinson’s disease (PD). Manifesting FOG episodes reduce patients’ quality of life and their autonomy to perform daily living activities, while it may provoke falls. Accurate ambulatory FOG assessment would enable non-pharmacologic support based on cues and would provide relevant information to neurologists on the disease evolution.
This paper presents a method for FOG detection based on deep learning and signal processing techniques. This is, to the best of our knowledge, the first time that FOG detection is addressed with deep learning. The evaluation of the model has been done based on the data from 15 PD patients who manifested FOG. An inertial measurement unit placed at the left side of the waist recorded tri-axial accelerometer, gyroscope and magnetometer signals. Our approach achieved comparable results to the state-of-the-art, reaching validation performances of 88.6% and 78% for sensitivity and specificity respectively.

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previous chapter Posture Transitions Identification Based on a Triaxial Accelerometer and a Barometer Sensor
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Footnotes
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Metadata
Title
Deep Learning for Detecting Freezing of Gait Episodes in Parkinson’s Disease Based on Accelerometers
Authors
Julià Camps
Albert Samà
Mario Martín
Daniel Rodríguez-Martín
Carlos Pérez-López
Sheila Alcaine
Berta Mestre
Anna Prats
M. Cruz Crespo
Joan Cabestany
Àngels Bayés
Andreu Català
Copyright Year
2017
Book
Advances in Computational Intelligence

Print ISBN: 978-3-319-59146-9

Electronic ISBN: 978-3-319-59147-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59147-6_30

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