Ambulatory mobility characterization using body inertial system: an application to fall detection

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/9919

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor	Universitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement
dc.contributor.author	Torrent Poch, Marc
dc.contributor.author	Bourke, Alan
dc.contributor.author	Llanas Parra, Francesc Xavier
dc.contributor.author	Català Mallofré, Andreu
dc.date	2009
dc.identifier.citation	Torrent, M. [et al.]. Ambulatory mobility characterization using body inertial system: an application to fall detection. A: International Work-Conference on Artificial Neural Networks. "10th International Work-Conference on Artificial Neural Networks". Salamanca: Springer Verlag, 2009, p. 1129-1136.
dc.identifier.citation	978-3-642-02477-1
dc.identifier.uri	http://hdl.handle.net/2117/9919
dc.language.iso	eng
dc.publisher	Springer Verlag
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
dc.subject	Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadors
dc.subject	Biomedical applications of biotechnology
dc.subject	Biotechnology
dc.subject	Falls (Accidents) in old age--Prevention
dc.subject	Detectors
dc.subject	Caigudes (Accidents) en la vellesa
dc.subject	Detectors
dc.title	Ambulatory mobility characterization using body inertial system: an application to fall detection
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	The aim of this paper is to study the use of a prototype of wearable device for long term monitoring of gait and balance using inertial sensors. First, it is focused on the design of the device that can be used all day during the patient daily life activities, because it is small, usable and non invasive. Secondly, we present the system calibration to ensure the quality of the sensors data. Afterwodrs, we focus in the experimental methodology for data harvest from extensive types of falls. Finally a statistical analysis allows us to determine the discriminant information to detect falls.
dc.description.abstract	Peer Reviewed

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