5 Citazioni (Scopus)

Abstract

Nowadays, the population’s average age is constantly increasing, and thus the need for specialized home assistance is on the rise. Smart homes especially tailored to meet elderly and disabled people's needs can help them maintaining their autonomy, whilst ensuring their safety and well-being. This paper proposes a complete context-aware system for Ambient Assisted Living (AAL), which infers user's actions and context, analyzing its past and current behavior to detect anomalies and prevent possible emergencies. The proposed system exploits Dynamic Bayesian Networks to merge raw data coming from heterogeneous sensors and infer user's behavior and health conditions. A rule-based reasoner is able to detect and predict anomalies in such data, sending appropriate alerts to caregivers and family members. The effectiveness of the proposed AAL system is demonstrated by extensive experimental results carried out in a simulated smart home.
Lingua originaleEnglish
Numero di pagine6
Stato di pubblicazionePublished - 2018

Fingerprint

Bayesian networks
Dynamical systems
Health
Sensors
Ambient intelligence
Assisted living

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Biomedical Engineering
  • Renewable Energy, Sustainability and the Environment

Cita questo

@conference{c702ce5254634ac695291c890811fa3b,
title = "An Ambient Intelligence System for Assisted Living",
abstract = "Nowadays, the population’s average age is constantly increasing, and thus the need for specialized home assistance is on the rise. Smart homes especially tailored to meet elderly and disabled people's needs can help them maintaining their autonomy, whilst ensuring their safety and well-being. This paper proposes a complete context-aware system for Ambient Assisted Living (AAL), which infers user's actions and context, analyzing its past and current behavior to detect anomalies and prevent possible emergencies. The proposed system exploits Dynamic Bayesian Networks to merge raw data coming from heterogeneous sensors and infer user's behavior and health conditions. A rule-based reasoner is able to detect and predict anomalies in such data, sending appropriate alerts to caregivers and family members. The effectiveness of the proposed AAL system is demonstrated by extensive experimental results carried out in a simulated smart home.",
author = "Pierluca Ferraro and Daniele Peri and {De Paola}, Alessandra and Marco Ortolani and Salvatore Gaglio and Marco Morana and {Lo Re}, Giuseppe",
year = "2018",
language = "English",

}

TY - CONF

T1 - An Ambient Intelligence System for Assisted Living

AU - Ferraro, Pierluca

AU - Peri, Daniele

AU - De Paola, Alessandra

AU - Ortolani, Marco

AU - Gaglio, Salvatore

AU - Morana, Marco

AU - Lo Re, Giuseppe

PY - 2018

Y1 - 2018

N2 - Nowadays, the population’s average age is constantly increasing, and thus the need for specialized home assistance is on the rise. Smart homes especially tailored to meet elderly and disabled people's needs can help them maintaining their autonomy, whilst ensuring their safety and well-being. This paper proposes a complete context-aware system for Ambient Assisted Living (AAL), which infers user's actions and context, analyzing its past and current behavior to detect anomalies and prevent possible emergencies. The proposed system exploits Dynamic Bayesian Networks to merge raw data coming from heterogeneous sensors and infer user's behavior and health conditions. A rule-based reasoner is able to detect and predict anomalies in such data, sending appropriate alerts to caregivers and family members. The effectiveness of the proposed AAL system is demonstrated by extensive experimental results carried out in a simulated smart home.

AB - Nowadays, the population’s average age is constantly increasing, and thus the need for specialized home assistance is on the rise. Smart homes especially tailored to meet elderly and disabled people's needs can help them maintaining their autonomy, whilst ensuring their safety and well-being. This paper proposes a complete context-aware system for Ambient Assisted Living (AAL), which infers user's actions and context, analyzing its past and current behavior to detect anomalies and prevent possible emergencies. The proposed system exploits Dynamic Bayesian Networks to merge raw data coming from heterogeneous sensors and infer user's behavior and health conditions. A rule-based reasoner is able to detect and predict anomalies in such data, sending appropriate alerts to caregivers and family members. The effectiveness of the proposed AAL system is demonstrated by extensive experimental results carried out in a simulated smart home.

UR - http://hdl.handle.net/10447/250726

M3 - Other

ER -