Automatic fall detection for elderly by using features extracted from skeletal data

Amir Davari, Tarkan Aydin, Tanju Erdem

Araştırma sonucu: Kitap/Rapor/Konferans sürecindeki bölümKonferans katkısıbilirkişi

9 Alıntılar (Scopus)

Özet

Automatic detection of unusual events such as falls is very important especially for elderly people living alone. Realtime detection of these events can reduce the health risks associated with a fall. In this paper, we propose a novel method for automatic detection of fall event by using depth cameras. Depth images generated by these cameras are used in computing the skeletal data of a person. Our contribution is to use features extracted from the skeletal data to form a strong set of features which can help us achieve an increased precision at low redundancy. Our findings indicate that our features, which are derived from skeletal data, are moderately powerful for detecting unusual events such as fall.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2013 International Conference on Electronics, Computer and Computation, ICECCO 2013
YayınlayanIEEE Computer Society
Sayfalar127-130
Sayfa sayısı4
ISBN (Basılı)9781479933433
DOI'lar
Yayın durumuYayınlanan - 2013
Harici olarak yayınlandıEvet
Etkinlik2013 10th International Conference on Electronics, Computer and Computation, ICECCO 2013 - Ankara, Turkey
Süre: 7 Kas 20138 Kas 2013

Yayın serisi

Adı2013 International Conference on Electronics, Computer and Computation, ICECCO 2013

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???event.eventtypes.event.conference???2013 10th International Conference on Electronics, Computer and Computation, ICECCO 2013
Ülke/BölgeTurkey
ŞehirAnkara
Periyot7/11/138/11/13

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