Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors

Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson’s disease (PD). The occurrence of FOG reduces the patients’ quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provi...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 20; no. 7; p. 1895
Main Authors: Sigcha, Luis, Costa, Nélson, Pavón, Ignacio, Costa, Susana, Arezes, Pedro, López, Juan Manuel, De Arcas, Guillermo
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 29.03.2020
MDPI
Subjects:
accelerometer
Accelerometry - methods
Activities of daily living
Aged
Aged, 80 and over
Algorithms
Artificial intelligence
Biosensing Techniques
Cognition & reasoning
consecutive windows
convolutional neural networks
Deep Learning
denoising autoencoder
Female
Fog
Gait
Gait - physiology
Humans
IMU
LSTM
Male
Monitoring, Physiologic
Natural language processing
Neural networks
Parkinson Disease - diagnosis
Parkinson Disease - physiopathology
Parkinson's disease
Patients
Performance evaluation
Quality of life
Sensors
Signal Processing, Computer-Assisted
Support vector machines
Wearable Electronic Devices
IMU
consecutive windows
LSTM
time distributed
accelerometer
denoising autoencoder
convolutional neural networks
spectral representation
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson’s disease (PD). The occurrence of FOG reduces the patients’ quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms’ evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients’ homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20071895