TY - GEN
T1 - homeSound
T2 - 6th International Conference on Sensor Networks, SENSORNETS 2017
AU - Hervás, Marcos
AU - Alsina-Pagès, Rosa Ma
AU - Navarro, Joan
N1 - Funding Information:
The authors would like to thank the Secre-taria d’Universitats i Recerca del Departament d’Economia i Coneixement (Generalitat de Catalunya) under grant ref. 2014-SGR-0590.
Publisher Copyright:
Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Human life expectancy has steadily grown over the last century, which has driven governments and institutions to increase the efforts on caring about the eldest segment of the population. The first answer to that increasing need was the building of hospitals and retirement homes, but these facilities have been rapidly overfilled and their associated maintenance costs are becoming far prohibitive. Therefore, modern trends attempt to take advantage of latest advances in technology and communications to remotely monitor those people with special needs at their own home, increasing their life quality and with much less impact on their social lives. Nonetheless, this approach still requires a considerable amount of qualified medical personnel to track every patient at any time. The purpose of this paper is to present an acoustic event detection platform for assisted living that tracks patients status by automatically identifying and analyzing the acoustic events happening in a house. Specifically, we have taken benefit of the amazing capabilities of a Jetson TK1, with its NVIDIA Graphical Processing Unit, to collect the data in the house and process it to identify a closed number of events, which could led doctors or care assistants in real-time by tracking the patient at home. This is a proof of concept conducted with data of only one acoustic sensor, but in the future we have planned to extract information of the sensor network placed in several places in the house.
AB - Human life expectancy has steadily grown over the last century, which has driven governments and institutions to increase the efforts on caring about the eldest segment of the population. The first answer to that increasing need was the building of hospitals and retirement homes, but these facilities have been rapidly overfilled and their associated maintenance costs are becoming far prohibitive. Therefore, modern trends attempt to take advantage of latest advances in technology and communications to remotely monitor those people with special needs at their own home, increasing their life quality and with much less impact on their social lives. Nonetheless, this approach still requires a considerable amount of qualified medical personnel to track every patient at any time. The purpose of this paper is to present an acoustic event detection platform for assisted living that tracks patients status by automatically identifying and analyzing the acoustic events happening in a house. Specifically, we have taken benefit of the amazing capabilities of a Jetson TK1, with its NVIDIA Graphical Processing Unit, to collect the data in the house and process it to identify a closed number of events, which could led doctors or care assistants in real-time by tracking the patient at home. This is a proof of concept conducted with data of only one acoustic sensor, but in the future we have planned to extract information of the sensor network placed in several places in the house.
KW - Ambient Assisted Living
KW - Audio Feature Extraction
KW - Graphics Processor Unit
KW - Machine Hearing
KW - Machine Learning
KW - Sensor Network
UR - http://www.scopus.com/inward/record.url?scp=85018494446&partnerID=8YFLogxK
U2 - 10.5220/0006209701820187
DO - 10.5220/0006209701820187
M3 - Conference contribution
AN - SCOPUS:85018494446
T3 - SENSORNETS 2017 - Proceedings of the 6th International Conference on Sensor Networks
SP - 182
EP - 187
BT - SENSORNETS 2017 - Proceedings of the 6th International Conference on Sensor Networks
A2 - Fleury, Eric
A2 - Ahrens, Andreas
A2 - Benavente-Peces, Cesar
PB - SciTePress
Y2 - 19 February 2017 through 21 February 2017
ER -