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Estimating bradykinesia severity in Parkinson's disease by analysing gait through a waist-worn sensor
dc.contributor.author | Samà Monsonís, Albert |
dc.contributor.author | Pérez López, Carlos |
dc.contributor.author | Rodríguez Martín, Daniel Manuel |
dc.contributor.author | Català Mallofré, Andreu |
dc.contributor.author | Moreno Aróstegui, Juan Manuel |
dc.contributor.author | Cabestany Moncusí, Joan |
dc.contributor.author | De Mingo Fernandez, Eva |
dc.contributor.author | Rodríguez Molinero, Alejandro |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2017-04-28T07:48:31Z |
dc.date.available | 2021-05-01T00:25:39Z |
dc.date.issued | 2017-05-01 |
dc.identifier.citation | Sama, A., Perez, C., Rodriguez-Martin, D., Catala, A., Moreno, J., Cabestany, J., De Mingo, E., Rodríguez, A. Estimating bradykinesia severity in Parkinson's disease by analysing gait through a waist-worn sensor. "Computers in biology and medicine", 1 Maig 2017, vol. 84, p. 114-123. |
dc.identifier.issn | 0010-4825 |
dc.identifier.uri | http://hdl.handle.net/2117/103824 |
dc.description.abstract | Bradykinesia is a cardinal symptom of Parkinson's disease (PD) and describes the slowness of movement revealed in patients. Current PD therapies are based on dopamine replacement, and given that bradykinesia is the symptom that best correlates with the dopaminergic deficiency, the knowledge of its fluctuations may be useful in the diagnosis, treatment and better understanding of the disease progression. This paper evaluates a machine learning method that analyses the signals provided by a triaxial accelerometer placed on the waist of PD patients in order to automatically assess bradykinetic gait unobtrusively. This method employs Support Vector Machines to determine those parts of the signals corresponding to gait. The frequency content of strides is then used to determine bradykinetic walking bouts and to estimate bradykinesia severity based on an epsilon-Support Vector Regression model. The method is validated in 12 PD patients, which leads to two main conclusions. Firstly, the frequency content of the strides allows for the dichotomic detection of bradykinesia with an accuracy higher than 90%. This process requires the use of a patient-dependant threshold that is estimated based on a leave-one-patient-out regression model. Secondly, bradykinesia severity measured through UPDRS scores is approximated by means of a regression model with errors below 10%. Although the method has to be further validated in more patients, results obtained suggest that the presented approach can be successfully used to rate bradykinesia in the daily life of PD patients unobtrusively. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | Elsevier |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria biomèdica |
dc.subject | Àrees temàtiques de la UPC::Ciències de la salut |
dc.subject | Àrees temàtiques de la UPC::Enginyeria electrònica |
dc.subject.lcsh | Support vector machines |
dc.subject.lcsh | Biosensors |
dc.subject.lcsh | Parkinson's disease -- Research |
dc.subject.lcsh | Self-help devices for people with disabilities |
dc.subject.other | Support Vector Machines |
dc.subject.other | Inertial sensors |
dc.subject.other | Bradykinesia |
dc.subject.other | Parkinson's disease |
dc.title | Estimating bradykinesia severity in Parkinson's disease by analysing gait through a waist-worn sensor |
dc.type | Article |
dc.subject.lemac | Parkinson, Malaltia de |
dc.subject.lemac | Ajuts tecnològics per als discapacitats |
dc.subject.lemac | Biosensors |
dc.contributor.group | Universitat Politècnica de Catalunya. CETpD -Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma |
dc.identifier.doi | 10.1016/j.compbiomed.2017.03.020 |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/abs/pii/S0010482517300756 |
dc.rights.access | Open Access |
local.identifier.drac | 19857510 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | MoMoPa2 Project (Monitoring the mobility of Parkinson’s patients for therapeutic purposes 2 - PI12/03028) funded by the Instituto de Salud Carlos III - Ministerio de Economía y Competividad and the European Regional Development Fund (ERDF) |
dc.relation.projectid | Monitoring the Mobility of Parkinson’s Patients for Therapeutic Purposes Project (DTS15/00209), funded by the Instituto de Salud Carlos III - Ministerio de Economía, Industria y Competitividad and the European Regional Development Fund. |
local.citation.author | Sama, A.; Perez, C.; Rodriguez-Martin, D.; Catala, A.; Moreno, J.; Cabestany, J.; De Mingo, E.; Rodríguez, A. |
local.citation.publicationName | Computers in biology and medicine |
local.citation.volume | 84 |
local.citation.startingPage | 114 |
local.citation.endingPage | 123 |
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