Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Hungler, Paul"'
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
Cognitive load, the amount of mental effort required for task completion, plays an important role in performance and decision-making outcomes, making its classification and analysis essential in various sensitive domains. In this paper, we present a
Externí odkaz:
http://arxiv.org/abs/2308.00246
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 propose cross-modal attentive connections, a new dynamic and effective technique for multimodal representation learning from wearable data. Our solution can be integrated into any stage of the pipeline, i.e., after any convolutional layer or block
Externí odkaz:
http://arxiv.org/abs/2206.04625
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
Electrocardiogram (ECG) has been widely used for emotion recognition. This paper presents a deep neural network based on convolutional layers and a transformer mechanism to detect stress using ECG signals. We perform leave-one-subject-out experiments
Externí odkaz:
http://arxiv.org/abs/2108.09737
Classification of human emotions can play an essential role in the design and improvement of human-machine systems. While individual biological signals such as Electrocardiogram (ECG) and Electrodermal Activity (EDA) have been widely used for emotion
Externí odkaz:
http://arxiv.org/abs/2108.02241
With recent developments in smart technologies, there has been a growing focus on the use of artificial intelligence and machine learning for affective computing to further enhance the user experience through emotion recognition. Typically, machine l
Externí odkaz:
http://arxiv.org/abs/2008.10726
Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning
Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills. Such simulations should provide an optimum amount of cognitive load to the learner and be tailored
Externí odkaz:
http://arxiv.org/abs/1908.00385