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TitleDeep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors
Author(s)Sigcha, Luis
Costa, Nélson
Pavón, Ignacio
Costa, Susana Raquel Pinto
Arezes, P.
López, Juan Manuel
De Arcas, Guillermo
convolutional neural networks
consecutive windows
denoising autoencoder
time distributed
spectral representation
Issue date29-Mar-2020
PublisherMultidisciplinary Digital Publishing Institute
CitationSigcha, L.; Costa, N.; Pavón, I.; Costa, S.; Arezes, P.; López, J.M.; De Arcas, G. Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors. Sensors 2020, 20, 1895.
Abstract(s)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).
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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