Detecting freezing of gait with a tri-axial accelerometer in Parkinson's disease patients.
Freezing 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 th...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 54; no. 1; pp. 223 - 234 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jan2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=113529494&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113529494 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2016 vid: 54 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 113529494 113529494 NLM26429349 113529494 10.1007/s11517-015-1395-3 NLM26429349 113529494 ppf: 223 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Detecting freezing of gait with a tri-axial accelerometer in Parkinson's disease patients. aug: au: Ahlrichs, Claas Samà, Albert Lawo, Michael Cabestany, Joan Rodríguez-Martín, Daniel Pérez-López, Carlos Sweeney, Dean Quinlan, Leo Laighin, Gearòid Counihan, Timothy Browne, Patrick Hadas, Lewy Vainstein, Gabriel Costa, Alberto Annicchiarico, Roberta Alcaine, Sheila Mestre, Berta Quispe, Paola Bayes, Àngels Rodríguez-Molinero, Alejandro affil: neusta mobile solutions GmbH (NMS), Konsul-Smidt-Str. 24 28217 Bremen Germany sug: subj: Parkinson Disease Physiopathology Gait Accelerometry Methods Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Freezing 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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