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Article

homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring

1
GTM—Grup de recerca en Tecnologies Mèdia, La Salle—Universitat Ramon Llull, C/Quatre Camins, 30, 08022 Barcelona, Catalonia, Spain
2
GRITS—Grup de Recerca en Internet Technologies & Storage, La Salle—Universitat Ramon Llull, C/Quatre Camins, 30, 08022 Barcelona, Catalonia, Spain
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(4), 854; https://doi.org/10.3390/s17040854
Received: 22 February 2017 / Revised: 29 March 2017 / Accepted: 10 April 2017 / Published: 13 April 2017
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
The 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. View Full-Text
Keywords: Ambient Assisted Living; Acoustic Sensor Network; machine hearing; audio feature extraction; data mining; graphics processor unit; behaviour monitoring; surveillance Ambient Assisted Living; Acoustic Sensor Network; machine hearing; audio feature extraction; data mining; graphics processor unit; behaviour monitoring; surveillance
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MDPI and ACS Style

Alsina-Pagès, R.M.; Navarro, J.; Alías, F.; Hervás, M. homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring. Sensors 2017, 17, 854. https://doi.org/10.3390/s17040854

AMA Style

Alsina-Pagès RM, Navarro J, Alías F, Hervás M. homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring. Sensors. 2017; 17(4):854. https://doi.org/10.3390/s17040854

Chicago/Turabian Style

Alsina-Pagès, Rosa M., Joan Navarro, Francesc Alías, and Marcos Hervás. 2017. "homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring" Sensors 17, no. 4: 854. https://doi.org/10.3390/s17040854

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