Automated Audio Data Monitoring for a Social Robot in Ambient Assisted Living Environments

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. Although this concern was initially addressed by building larger hospitals and retirement homes, these facilitie...

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Bibliographic Details
Authors: Alsina-Pagès, Rosa Ma, Navarro, Joan, Casals Oliveras, Enric
Format: article
Status:Published version
Publication Date:2016
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:20.500.14342/2877
Online Access:http://hdl.handle.net/20.500.14342/2877
Access Level:Open access
Keyword:Interacció persona-robot
Robòtica -- Factors humans
Description
Summary: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. Although this concern was initially addressed by building larger hospitals and retirement homes, 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, which boosts their life quality and has very few 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 a social robot 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 Raspberry Pi together with a Nao robot to collect data inside a house and send it in realtime to the medical center. Conducted experiments verify the feasibility of our approach and open new research directions in this domain.