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dc.contributor.authorAhlrichs, Claas
dc.contributor.authorSamà Monsonís, Albert
dc.contributor.authorLawo, Michael
dc.contributor.authorCabestany Moncusí, Joan
dc.contributor.authorRodríguez Martín, Daniel Manuel
dc.contributor.authorPérez López, Carlos
dc.contributor.authorQuinlan, Leo R.
dc.contributor.authorÓLaighin, Gearóid
dc.contributor.authorCounihan, Timothy
dc.contributor.authorLewy, Hadas
dc.contributor.authorAnnicchiarico, Roberta
dc.contributor.authorBayés, Àngels
dc.contributor.authorRodríguez Molinero, Alejandro
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.identifier.citationAhlrichs, C., Sama, A., Lawo, M., Cabestany, J., Rodriguez-Martin, D., Perez, C., Quinlan, L., ÓLaighin, G., Counihan, T., Lewy, H., Annicchiarico, R., Bayés, À., Rodríguez, A. Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients. "Medical and biological engineering and computing", 01 Octubre 2015, p. 1-11.
dc.description.abstractFreezing of gait (FOG) is a common motor symptom of Parkinson’s disease (PD), which presents itself as an inability to initiate or continue gait. This paper presents a method to monitor FOG episodes based only on acceleration measurements obtained from a waist-worn device. Three approximations of this method are tested. Initially, FOG is directly detected by a support vector machine (SVM). Then, classifier’s outputs are aggregated over time to determine a confidence value, which is used for the final classification of freezing (i.e., second and third approach). All variations are trained with signals of 15 patients and evaluated with signals from another 5 patients. Using a linear SVM kernel, the third approach provides 98.7 % accuracy and a geometric mean of 96.1 %. Moreover, it is investigated whether frequency features are enough to reliably detect FOG. Results show that these features allow the method to detect FOG with accuracies above 90 % and that frequency features enable a reliable monitoring of FOG by using simply a waist sensor.
dc.format.extent11 p.
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica
dc.subject.lcshParkinson's disease
dc.subject.otherParkinson’s disease Freezing of Gait Machine learning Support vector machines
dc.titleDetecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients
dc.subject.lemacParkinson, Malaltia de
dc.subject.lemacEnginyeria biomèdica
dc.contributor.groupUniversitat Politècnica de Catalunya. CETpD -Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
local.citation.authorAhlrichs, C.; Sama, A.; Lawo, M.; Cabestany, J.; Rodriguez-Martin, D.; Perez, C.; Quinlan, L.; ÓLaighin, G.; Counihan, T.; Lewy, H.; Annicchiarico, R.; Bayés, À.; Rodríguez, A.
local.citation.publicationNameMedical and biological engineering and computing

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