The life expectancy is rapidly growing in many countries. According to the United Nations, the percentage of elderly population will rise from 5% in 2013 to 11% in 2050. The increasing aging of the population implies an increase of age-related diseases, and an increase in terms of health-care costs. The innovations introduced by pervasive computing, and in particular by sensor-based activity monitoring methods, can be exploited to early detect the onset of health issues. For this reason, we devised a novel method to recognize anomalies that a senior performs during the execution of activities of daily living, based on data acquired from unobtrusive sensors deployed at home. The objective is to support the clinicians in the early diagnosis of neurodegenerative diseases, providing them with fine-grained information about abnormal behaviors. In this paper, we present a demonstration of the method, based on a graphical tool that simulates the execution of activities and abnormal behaviors of an elderly person in a sensor-rich smart home.

Demo abstract: Demonstration of the FABER system for fine-grained recognition of abnormal behaviors

RIBONI, DANIELE;
2015

Abstract

The life expectancy is rapidly growing in many countries. According to the United Nations, the percentage of elderly population will rise from 5% in 2013 to 11% in 2050. The increasing aging of the population implies an increase of age-related diseases, and an increase in terms of health-care costs. The innovations introduced by pervasive computing, and in particular by sensor-based activity monitoring methods, can be exploited to early detect the onset of health issues. For this reason, we devised a novel method to recognize anomalies that a senior performs during the execution of activities of daily living, based on data acquired from unobtrusive sensors deployed at home. The objective is to support the clinicians in the early diagnosis of neurodegenerative diseases, providing them with fine-grained information about abnormal behaviors. In this paper, we present a demonstration of the method, based on a graphical tool that simulates the execution of activities and abnormal behaviors of an elderly person in a sensor-rich smart home.
9781479984251
Computer networks and communications; Computer science applications; Computer vision and pattern recognition; Human-computer interaction; Health (social science)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/196858
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