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Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit
Camps, Julià; Samà Monsonís, Albert; Martín Muñoz, Mario; Rodríguez Martín, Daniel Manuel; Pérez López, Carlos; Moreno Aróstegui, Juan Manuel; Cabestany Moncusí, Joan; Català Mallofré, Andreu; Alcaine, Sheila; Mestre, Berta; Prats, Anna; Crespo, M. Cruz; Counihan, Timothy; Browne, Patrick; Quinlan, Leo R.; ÓLaighin, Gearóid; Sweeney, Dean; Lewy, Hadas; Vainstein, Gabriel; Costa, Alberto; Annicchiarico, Roberta; Bayés, Àngels; Rodríguez Molinero, Alejandro
Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica; Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. CETpD - Centre d’Estudis Tecnològics per a l’Atenció a la Dependència i la Vida Autònoma; Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient’s condition and the symptom’s characteristics, while it could enable non-pharmacologic support based on rhythmic cues. This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system. The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
Biomedical engineering
Parkinson's disease
Patient monitoring
Deep learning
Signal processing
Freezing of gait
Parkinson’s disease
Wearable device
Enginyeria biomèdica
Parkinson, Malaltia de
Monitoratge de pacients
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/publishedVersion
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