Document details

Sensor-driven agenda for intelligent home care of the elderly

Author(s): Costa, Angelo cv logo 1 ; Castillo, José Carlos cv logo 2 ; Novais, Paulo cv logo 3 ; Fernández-Caballero, Antonio cv logo 4 ; Simões, Ricardo cv logo 5

Date: 2012

Persistent ID: http://hdl.handle.net/11110/302

Origin: CiencIPCA

Subject(s): Home care; Elderly; Monitoring; Personal agenda; Intelligent agents


Description
The exponential increase of home-bound persons who live alone and are in need of continuous monitoring requires new solutions to current problems. Most of these cases present illnesses such as motor or psychological disabilities that deprive of a normal living. Common events such as forgetfulness or falls are quite common and have to be prevented or dealt with. This paper introduces a platform to guide and assist these persons (mostly elderly people) by providing multisensory monitoring and intelligent assistance. The platform operates at three levels. The lower level, denominated ‘‘Data acquisition and processing’’performs the usual tasks of a monitoring system, collecting and processing data from the sensors for the purpose of detecting and tracking humans. The aim is to identify their activities in an intermediate level called ‘‘activity detection’’. The upper level, ‘‘Scheduling and decision-making’’, consists of a scheduler which provides warnings, schedules events in an intelligent manner and serves as an interface to the rest of the platform. The idea is to use mobile and static sensors performing constant monitoring of the user and his/her environment, providing a safe environment and an immediate response to severe problems. A case study on elderly fall detection in a nursery home bedroom demonstrates the usefulness of the proposal. This work is partially supported by the Spanish Ministerio de Economía y Competitividad/FEDER under Project TIN2010-20845-C03-01 and by the Spanish Junta de Comunidades de Castilla-La Mancha/FEDER under Project PII2I09-0069-0994. Also in collaboration with the AAL4ALL QREN 13852 project.
Document Type Article
Language English
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