Abstract

The chapter presents an automated monitoring system for the detection of dangerous events of elderly people (such as falls) in AAL applications. In order to provide a self-contained technology solution not requiring neither the environment rearrangement, nor the presence of specialized staff, nor a priori information about elderly characteristics/habitude, the focus is placed on the classification of human postures and the detection of related adverse events. The people is detected through a non-wearable device (a TOF camera), overcoming the limitations of the wearable approaches (accelerometers, gyroscopes, etc.) for human monitoring (the devices are prone to be incorrectly worn or forgotten). The system shows high performances in terms of efficiency and reliability on a large real dataset of falls acquired in different conditions. The posture recognition is carried out by using a topological approach on the 3D points cloud. Experimental results validate the soundness of the posture recognition scheme. © 2011 Springer Science+Business Media B.V.

Autore Pugliese

• Leone Alessandro , Diraco Giovanni , Siciliano Pietro Aleardo

Tutti gli autori

• Leone A.; Diraco G.; Siciliano P.

Titolo volume/Rivista

Lecture notes in electrical engineering

Anno di pubblicazione

2011

ISSN

1876-1100

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

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Numero di citazioni Scopus

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Settori ERC

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Codici ASJC

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