DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations, it is also difficult to extract domain-...

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Veröffentlicht in:	Journal of healthcare informatics research Jg. 4; H. 1; S. 50 - 70
Hauptverfasser:	Nogas, Jacob, Khan, Shehroz S., Mihailidis, Alex
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.03.2020
Schlagworte:	Biomedical Engineering and Bioengineering Computational Biology/Bioinformatics Computational Intelligence Computer Science Data Mining and Knowledge Discovery Health Informatics Research Article Spatio-temporal Convolutional autoencoders Fall detection Anomaly detection
ISSN:	2509-4971, 2509-498X
Online-Zugang:	Volltext
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