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Athanasios Bamis
Researcher at Yale University
Publications - 35
Citations - 817
Athanasios Bamis is an academic researcher from Yale University. The author has contributed to research in topics: Wireless sensor network & Authentication. The author has an hindex of 15, co-authored 34 publications receiving 668 citations. Previous affiliations of Athanasios Bamis include Amazon.com & University of Patras.
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Proceedings ArticleDOI
Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data
Asma Ahmad Farhan,Chaoqun Yue,Reynaldo Morillo,Shweta Ware,Jin Lu,Jinbo Bi,Jayesh Kamath,Alexander Russell,Athanasios Bamis,Bing Wang +9 more
TL;DR: This study finds that behavioral data from smartphones can predict clinical depression with good accuracy, and develops multi-feature regression models for PHQ-9 scores that achieve significantly improved accuracy compared to direct regression models based on single features.
Journal ArticleDOI
The BehaviorScope framework for enabling ambient assisted living
TL;DR: This framework supports in-house monitoring of elders using an intelligent gateway and a set of cheap commercially available sensors, in addition to more advanced camera-based human localization sensors and a client for GPS-enabled mobile phones that provides monitoring when outdoors.
Journal ArticleDOI
A mobility aware protocol synthesis for efficient routing in ad hoc mobile networks
TL;DR: This work builds upon recent results on the effect of node mobility on the performance of available routing strategies and proposes a protocol framework that exploits the usually different mobility rates of the nodes by adapting the routing strategy during execution.
Journal ArticleDOI
Extracting spatiotemporal human activity patterns in assisted living using a home sensor network
TL;DR: An automated methodology for extracting the spatiotemporal activity model of a person using a wireless sensor network deployed inside a home using an exhaustive search algorithm is presented.
Journal ArticleDOI
Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning
Jin Lu,Chao Shang,Chaoqun Yue,Reynaldo Morillo,Shweta Ware,Jayesh Kamath,Athanasios Bamis,Alexander Russell,Bing Wang,Jinbo Bi +9 more
TL;DR: This work proposes a so-called heterogeneous multi-task feature learning method that jointly builds inference models for related tasks but of different types including classification and regression tasks and identified strong depression indicators such as time staying at home and total time asleep.