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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| Published in: | Sensors (Basel, Switzerland) Vol. 20; no. 7; p. 1895 |
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| Abstract | 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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| AbstractList | 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). 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).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). |
| Author | Arezes, Pedro López, Juan Manuel Costa, Nélson Pavón, Ignacio Costa, Susana Sigcha, Luis De Arcas, Guillermo |
| AuthorAffiliation | 2 ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; ncosta@dps.uminho.pt (N.C.); susana.costa@dps.uminho.pt (S.C.); parezes@dps.uminho.pt (P.A.) 1 Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; luisfrancisco.sigcha@upm.es (L.S.); juanmanuel.lopez@upm.es (J.M.L.); g.dearcas@upm.es (G.D.A.) |
| AuthorAffiliation_xml | – name: 2 ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; ncosta@dps.uminho.pt (N.C.); susana.costa@dps.uminho.pt (S.C.); parezes@dps.uminho.pt (P.A.) – name: 1 Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; luisfrancisco.sigcha@upm.es (L.S.); juanmanuel.lopez@upm.es (J.M.L.); g.dearcas@upm.es (G.D.A.) |
| Author_xml | – sequence: 1 givenname: Luis orcidid: 0000-0002-9968-5024 surname: Sigcha fullname: Sigcha, Luis – sequence: 2 givenname: Nélson orcidid: 0000-0002-9348-8038 surname: Costa fullname: Costa, Nélson – sequence: 3 givenname: Ignacio orcidid: 0000-0003-0970-0452 surname: Pavón fullname: Pavón, Ignacio – sequence: 4 givenname: Susana orcidid: 0000-0001-7440-8787 surname: Costa fullname: Costa, Susana – sequence: 5 givenname: Pedro orcidid: 0000-0001-9421-9123 surname: Arezes fullname: Arezes, Pedro – sequence: 6 givenname: Juan Manuel orcidid: 0000-0001-7847-8707 surname: López fullname: López, Juan Manuel – sequence: 7 givenname: Guillermo orcidid: 0000-0003-1699-7389 surname: De Arcas fullname: De Arcas, Guillermo |
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| Snippet | 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... 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... |
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| SubjectTerms | 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 |
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| Title | Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors |
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