Abstract

A novel ultra-wideband radar sensor system for simultaneous detection of falls and vital signs is presented. The suggested system is able to deal with real-life conditions, such as lack of real-fall data for training, body movements, several people present, and privacy issues. Micro-Doppler features, extracted from time-frequency spectrograms, are used to classify human actions as related to normal or abnormal activities (falls). A deep learning framework is used to extract and classify such features, also taking into account the specific way the older adult performs activity-of-daily-living actions. Preliminary validation results are very encouraging, showing the effectiveness to achieve good detection performance in assisted living scenarios.

Autore Pugliese

• Leone Alessandro

Tutti gli autori

• Leone A.; Diraco G.; Siciliano P.

Titolo volume/Rivista

Proceedings of IEEE Sensors ...

Anno di pubblicazione

2017

ISSN

1930-0395

ISBN

9781509010127

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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