Hierarchical extreme puzzle learning machine-based emotion recognition using multimodal physiological signals
A Pradhan, S Srivastava - Biomedical Signal Processing and Control, 2023 - Elsevier
Detection of exact emotions through multi-modal physiological signals provides relevant
information for different processes. Numerous computational approaches have been …
information for different processes. Numerous computational approaches have been …
Exploring the landscape of ubiquitous in-home health monitoring: a comprehensive survey
F Pourpanah, A Etemad - ACM Transactions on Computing for …, 2023 - dl.acm.org
Ubiquitous in-home health monitoring systems have become popular in recent years due to
the rise of digital health technologies and the growing demand for remote health monitoring …
the rise of digital health technologies and the growing demand for remote health monitoring …
Hybrid densenet with long short-term memory model for multi-modal emotion recognition from physiological signals
A Pradhan, S Srivastava - Multimedia Tools and Applications, 2024 - Springer
Recognition of emotions from multi-modal physiological signals is one among the toughest
tasks prevailing amid the research communities. Most existing works have focused on …
tasks prevailing amid the research communities. Most existing works have focused on …
Federated learning in Emotion Recognition Systems based on physiological signals for privacy preservation: a review
Abstract Automated Emotion Recognition Systems (ERS) with physiological signals help
improve health and decision-making in everyday life. It uses traditional Machine Learning …
improve health and decision-making in everyday life. It uses traditional Machine Learning …
Deep Learning-Based Automated Emotion Recognition Using Multi modal Physiological Signals and Time-Frequency Methods
Accurate prediction and recognition of human emotions are crucial for effective human-
computer interfaces. An Automated Emotion Recognition (AER) method is highly desirable …
computer interfaces. An Automated Emotion Recognition (AER) method is highly desirable …
Incongruity-aware multimodal physiology signals fusion for emotion recognition
J Li, N Chen, H Zhu, G Li, Z Xu, D Chen - Information Fusion, 2024 - Elsevier
Various physiological signals can reflect the human's emotional states objectively. How to
take advantage of the common as well as complementary properties of different …
take advantage of the common as well as complementary properties of different …
A hybrid transposed attention based deep learning model for wearable and explainable stress recognition
Stress is a prevalent issue in modern society which affects a large percentage of the
population. Stress has negative effects on human daily lives such as reduced memory and …
population. Stress has negative effects on human daily lives such as reduced memory and …
CLARE: Cognitive Load Assessment in REaltime with Multimodal Data
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 …
(CLARE). The dataset contains physiological and gaze data from 24 participants with self …
Emotion classification using electrocardiogram and machine learning: A study on the effect of windowing techniques
PK Govarthan, SK Peddapalli, N Ganapathy… - Expert Systems with …, 2024 - Elsevier
Automated emotion recognition using physiological signals has gained significant attention
in recent years due to its potential applications in human–computer interaction, healthcare …
in recent years due to its potential applications in human–computer interaction, healthcare …
Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting
Low-Rank Adaptation (LoRA) is a widely used technique for fine-tuning large pre-trained or
foundational models across different modalities and tasks. However, its application to time …
foundational models across different modalities and tasks. However, its application to time …