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pro vyhledávání: '"Mahmud, Zunayed"'
Autor:
Bhatti, Anubhav, Angkan, Prithila, Behinaein, Behnam, Mahmud, Zunayed, Rodenburg, Dirk, Braund, Heather, Mclellan, P. James, Ruberto, Aaron, Harrison, Geoffery, Wilson, Daryl, Szulewski, Adam, Howes, Dan, Etemad, Ali, Hungler, Paul
We present a novel multimodal dataset for Cognitive Load Assessment in REaltime (CLARE). The dataset contains physiological and gaze data from 24 participants with self-reported cognitive load scores as ground-truth labels. The dataset consists of fo
Externí odkaz:
http://arxiv.org/abs/2404.17098
Autor:
Angkan, Prithila, Behinaein, Behnam, Mahmud, Zunayed, Bhatti, Anubhav, Rodenburg, Dirk, Hungler, Paul, Etemad, Ali
Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signals such as Electrocardiography (ECG) and Electrodermal Activity (EDA) a
Externí odkaz:
http://arxiv.org/abs/2304.04273
Publikováno v:
IEEE Transactions on Artificial Intelligence, 2024
We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed solution comprises two components, first a network for isolating anatomic
Externí odkaz:
http://arxiv.org/abs/2206.09256
We present a novel multistream network that learns robust eye representations for gaze estimation. We first create a synthetic dataset containing eye region masks detailing the visible eyeball and iris using a simulator. We then perform eye region se
Externí odkaz:
http://arxiv.org/abs/2112.07878
Akademický článek
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