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SVM-based posture identification with a single waist-located triaxial accelerometer
dc.contributor.author | Rodríguez Martín, Daniel Manuel |
dc.contributor.author | Samà Monsonís, Albert |
dc.contributor.author | Pérez López, Carlos |
dc.contributor.author | Català Mallofré, Andreu |
dc.contributor.author | Cabestany Moncusí, Joan |
dc.contributor.author | Rodríguez Molinero, Alejandro |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
dc.date.accessioned | 2013-10-03T10:43:00Z |
dc.date.created | 2013-12 |
dc.date.issued | 2013-12 |
dc.identifier.citation | Rodriguez, D. [et al.]. SVM-based posture identification with a single waist-located triaxial accelerometer. "Expert systems with applications", Desembre 2013, vol. 40, núm. 18, p. 7203-7211. |
dc.identifier.issn | 0957-4174 |
dc.identifier.uri | http://hdl.handle.net/2117/20276 |
dc.description.abstract | Analysis of human body movement is an important research area, specially for health applications. In order to assess the quality of life of people with mobility problems like Parkinson’s disease o stroke patients, it is crucial to monitor and assess their daily life activities. The main goal of this work is the characterization of basic activities using a single triaxial accelerometer located at the waist. This paper presents a novel postural detection algorithm based in SVM methods which is able to detect and identify Walking, Stand, Sit, Lying, Sit to Stand, Stand to sit, Bending up/down, Lying from Sit and Sit from Lying transitions with a sensitivity of 97% and specificity of 84% with 2884 postures analyzed from 31 healthy volunteers. Parameters and models found have been tested in another dataset from Parkinson’s disease patients, achieving results of 98% of sensitivity and 78% of specificity in postural transitions. The proposed algorithm has been optimized to be easily implemented in real-time system for on-line monitoring applications. |
dc.format.extent | 9 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts |
dc.subject.lcsh | Expert systems applications |
dc.subject.other | Support vector machines Accelerometers Neurodegenerative diseases Real time systems |
dc.title | SVM-based posture identification with a single waist-located triaxial accelerometer |
dc.type | Article |
dc.subject.lemac | Sistemes experts (Informàtica) -- Aplicacions mèdiques |
dc.contributor.group | Universitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement |
dc.contributor.group | Universitat Politècnica de Catalunya. AHA - Arquitectures Hardware Avançades |
dc.identifier.doi | 10.1016/j.eswa.2013.07.028 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0957417413005058# |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 12775767 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/287677/EU/Personal Health Device for the Remote and Autonomous Management of Parkinson’s Disease/REMPARK |
dc.date.lift | 10000-01-01 |
local.citation.author | Rodriguez, D.; Sama, A.; Perez, C.; Catala, A.; Cabestany, J.; Rodríguez, A. |
local.citation.publicationName | Expert systems with applications |
local.citation.volume | 40 |
local.citation.number | 18 |
local.citation.startingPage | 7203 |
local.citation.endingPage | 7211 |
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