Abstract

Currently available technological solutions do not allow to reliably detect falls in the elderly, due to still-open issues on both sensing and processing sides. In fact, the most promising sensing approaches raise concerns for privacy issues (e.g., vision-based approaches) or low acceptability rate (e.g., wearable-based approaches); whereas on the processing side, commonly used methodologies are based on supervised techniques trained with both positive (falls) and negative (non-fall) samples, both simulated by healthy young subjects. As a result of such a training protocol, fall detectors inevitably exhibit lower performance when used in real-life conditions, in which monitored subjects are older adults. In order to address the problem of fall detection under real-life conditions, this study investigates privacy-preserving unobtrusive sensing technologies together with an unsupervised methodology trained exclusively on daily activity (non-fall) data from the monitored elderly subject. Preliminary results are very encouraging, showing the effectiveness to achieve a good detection performance and, most importantly, which is more reproducible in real-life scenarios.

Autore Pugliese

• Leone Alessandro , Diraco Giovanni

Tutti gli autori

• Leone A.; Diraco G.; Siciliano P.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2016

ISSN

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ISBN

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Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

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Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

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