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dc.contributorUniversitat Ramon Llull. La Salle
dc.contributor.authorAlsina Pagès, Rosa Maria
dc.contributor.authorNavarro Martín, Joan
dc.contributor.authorAlías Pujol, Francesc
dc.contributor.authorHervás García, Marcos
dc.date.accessioned2020-03-25T06:19:43Z
dc.date.accessioned2023-10-02T06:45:12Z
dc.date.available2020-03-25T06:19:43Z
dc.date.available2023-10-02T06:45:12Z
dc.date.created2017-02
dc.date.issued2017-04
dc.identifier.urihttp://hdl.handle.net/20.500.14342/3452
dc.description.abstractThe consistent growth in human life expectancy during the recent years has driven governments and private organizations to increase the efforts in caring for the eldest segment of the population. These institutions have built hospitals and retirement homes that have been rapidly overfilled, making their associated maintenance and operating costs prohibitive. The latest advances in technology and communications envisage new ways to monitor those people with special needs at their own home, increasing their quality of life in a cost-affordable way. The purpose of this paper is to present an Ambient Assisted Living (AAL) platform able to analyze, identify, and detect specific acoustic events happening in daily life environments, which enables the medic staff to remotely track the status of every patient in real-time. Additionally, this tele-care proposal is validated through a proof-of-concept experiment that takes benefit of the capabilities of the NVIDIA Graphical Processing Unit running on a Jetson TK1 board to locally detect acoustic events. Conducted experiments demonstrate the feasibility of this approach by reaching an overall accuracy of 82% when identifying a set of 14 indoor environment events related to the domestic surveillance and patients’ behaviour monitoring field. Obtained results encourage practitioners to keep working in this direction, and enable health care providers to remotely track the status of their patients in real-time with non-invasive methods.eng
dc.format.extent22 p.cat
dc.language.isoengcat
dc.publisherMDPIcat
dc.relation.ispartofSensors. 2017, Vol. 17, No. 4cat
dc.rightsAttribution 4.0 International
dc.rights© L'autor/a
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceRECERCAT (Dipòsit de la Recerca de Catalunya)
dc.subject.otherAcúsitcacat
dc.subject.otherMineria de dadescat
dc.titlehomeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoringcat
dc.typeinfo:eu-repo/semantics/articlecat
dc.typeinfo:eu-repo/semantics/publishedVersioncat
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapcat
dc.identifier.doihttp://dx.doi.org/10.3390/s17040854cat


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/
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