2 Citazioni (Scopus)

Abstract

In the near future, the worldâs population will be characterized by an increasing average age, and consequently, the number of people requiring for a special household assistance will dramatically rise. In this scenario, smart homes will significantly help users to increase their quality of life, while maintaining a great level of autonomy. This paper presents a system for Ambient Assisted Living (AAL) capable of understanding context and userâs behavior by exploiting data gathered by a pervasive sensor network. The knowledge inferred by adopting a Bayesian knowledge extraction approach is exploited to disambiguate the collected observations, making the AAL system able to detect and predict anomalies in userâs behavior or health condition, in order to send appropriate alerts to family members and caregivers. Experimental results performed on a simulated smart home prove the effectiveness of the proposed system.
Lingua originaleEnglish
Pagine426-438
Numero di pagine13
Stato di pubblicazionePublished - 2017

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Ambient Assisted Living
Smart Home
User Behavior
Context-aware
Knowledge Extraction
Living Systems
Quality of Life
Sensor networks
Sensor Networks
Anomaly
Health
Predict
Scenarios
Experimental Results
Assisted living
Observation
Knowledge
Context
Family
Autonomy

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cita questo

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title = "A context-aware system for ambient assisted living",
abstract = "In the near future, the world{\^a}s population will be characterized by an increasing average age, and consequently, the number of people requiring for a special household assistance will dramatically rise. In this scenario, smart homes will significantly help users to increase their quality of life, while maintaining a great level of autonomy. This paper presents a system for Ambient Assisted Living (AAL) capable of understanding context and user{\^a}s behavior by exploiting data gathered by a pervasive sensor network. The knowledge inferred by adopting a Bayesian knowledge extraction approach is exploited to disambiguate the collected observations, making the AAL system able to detect and predict anomalies in user{\^a}s behavior or health condition, in order to send appropriate alerts to family members and caregivers. Experimental results performed on a simulated smart home prove the effectiveness of the proposed system.",
author = "Marco Morana and {Lo Re}, Giuseppe and Pierluca Ferraro and Marco Ortolani and Salvatore Gaglio and {De Paola}, Alessandra and Daniele Peri",
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T1 - A context-aware system for ambient assisted living

AU - Morana, Marco

AU - Lo Re, Giuseppe

AU - Ferraro, Pierluca

AU - Ortolani, Marco

AU - Gaglio, Salvatore

AU - De Paola, Alessandra

AU - Peri, Daniele

PY - 2017

Y1 - 2017

N2 - In the near future, the worldâs population will be characterized by an increasing average age, and consequently, the number of people requiring for a special household assistance will dramatically rise. In this scenario, smart homes will significantly help users to increase their quality of life, while maintaining a great level of autonomy. This paper presents a system for Ambient Assisted Living (AAL) capable of understanding context and userâs behavior by exploiting data gathered by a pervasive sensor network. The knowledge inferred by adopting a Bayesian knowledge extraction approach is exploited to disambiguate the collected observations, making the AAL system able to detect and predict anomalies in userâs behavior or health condition, in order to send appropriate alerts to family members and caregivers. Experimental results performed on a simulated smart home prove the effectiveness of the proposed system.

AB - In the near future, the worldâs population will be characterized by an increasing average age, and consequently, the number of people requiring for a special household assistance will dramatically rise. In this scenario, smart homes will significantly help users to increase their quality of life, while maintaining a great level of autonomy. This paper presents a system for Ambient Assisted Living (AAL) capable of understanding context and userâs behavior by exploiting data gathered by a pervasive sensor network. The knowledge inferred by adopting a Bayesian knowledge extraction approach is exploited to disambiguate the collected observations, making the AAL system able to detect and predict anomalies in userâs behavior or health condition, in order to send appropriate alerts to family members and caregivers. Experimental results performed on a simulated smart home prove the effectiveness of the proposed system.

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UR - http://springerlink.com/content/0302-9743/copyright/2005/

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EP - 438

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