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Found 8 papers, 1 papers with code
CLARE: Cognitive Load Assessment in REaltime with Multimodal Data
In contrast, for LOSO, the best performance is achieved by the deep learning model with ECG, EDA, and EEG.
SM70: A Large Language Model for Medical Devices
We are introducing SM70, a 70 billion-parameter Large Language Model that is specifically designed for SpassMed's medical devices under the brand name 'JEE1' (pronounced as G1 and means 'Life').
Vital Sign Forecasting for Sepsis Patients in ICUs
This paper uses state-of-the-art deep learning (DL) architectures to introduce a multi-step forecasting system to predict vital signs indicative of septic shock progression in Intensive Care Units (ICUs).
Interpreting Forecasted Vital Signs Using N-BEATS in Sepsis Patients
We analyze the samples where the forecasted trend does not match the actual trend and study the impact of infused drugs on changing the actual vital signs compared to the forecasted trend.
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines
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) as well as eye tracking data.
AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition
We perform extensive experiments on three public multimodal wearable datasets, WESAD, SWELL-KW, and CASE, and demonstrate that our method can effectively regulate and share information between different modalities to learn better representations.
A Transformer Architecture for Stress Detection from ECG
Electrocardiogram (ECG) has been widely used for emotion recognition.
Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition
Classification of human emotions can play an essential role in the design and improvement of human-machine systems.
