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dc.contributor.authorAhlrichs, Claas
dc.contributor.authorSamà, Albert
dc.contributor.authorLawo, Michael
dc.contributor.authorCabestany, Joan
dc.contributor.authorRodríguez-Martín, Daniel
dc.contributor.authorPérez-López, Carlos
dc.contributor.authorSweeney, Dean
dc.contributor.authorQuinlan, Leo R.
dc.contributor.authorLaighin, Gearòid Ò
dc.contributor.authorCounihan, Timothy
dc.contributor.authorBrowne, Patrick
dc.contributor.authorHadas, Lewy
dc.contributor.authorVainstein, Gabriel
dc.contributor.authorCosta, Alberto
dc.contributor.authorAnnicchiarico, Roberta
dc.contributor.authorAlcaine, Sheila
dc.contributor.authorMestre, Berta
dc.contributor.authorQuispe, Paola
dc.contributor.authorBayes, Àngels
dc.contributor.authorRodríguez-Molinero, Alejandro
dc.date.accessioned2018-09-20T15:59:13Z
dc.date.available2018-09-20T15:59:13Z
dc.date.issued2015-10-01
dc.identifier.citationAhlrichs, Claas; Samà, Albert; Lawo, Michael; Cabestany, Joan; Rodríguez-Martín, Daniel; Pérez-López, Carlos; Sweeney, Dean; Quinlan, Leo R. Laighin, Gearòid Ò; Counihan, Timothy; Browne, Patrick; Hadas, Lewy; Vainstein, Gabriel; Costa, Alberto; Annicchiarico, Roberta; Alcaine, Sheila; Mestre, Berta; Quispe, Paola; Bayes, Àngels; Rodríguez-Molinero, Alejandro (2015). Detecting freezing of gait with a tri-axial accelerometer in parkinson’s disease patients. Medical & Biological Engineering & Computing 54 (1), 223-233
dc.identifier.issn0140-0118,1741-0444
dc.identifier.urihttp://hdl.handle.net/10379/10167
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.publisherSpringer Nature
dc.relation.ispartofMedical & Biological Engineering & Computing
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectparkinson's disease
dc.subjectfreezing of gait
dc.subjectmachine learning
dc.subjectsupport vector machines
dc.subjectdiagnosis
dc.subjectsymptoms
dc.subjectmodels
dc.titleDetecting freezing of gait with a tri-axial accelerometer in parkinson’s disease patients
dc.typeArticle
dc.identifier.doi10.1007/s11517-015-1395-3
dc.local.publishedsourcehttps://upcommons.upc.edu/bitstream/2117/86472/1/template.pdf
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