A review of AI cloud and edge sensors, methods, and applications for the recognition of emotional, affective and physiological states
Affective, emotional, and physiological states (AFFECT) detection and recognition by
capturing human signals is a fast-growing area, which has been applied across numerous …
capturing human signals is a fast-growing area, which has been applied across numerous …
A large finer-grained affective computing EEG dataset
Affective computing based on electroencephalogram (EEG) has gained increasing attention
for its objectivity in measuring emotional states. While positive emotions play a crucial role in …
for its objectivity in measuring emotional states. While positive emotions play a crucial role in …
Advancements in Affective Disorder Detection: Using Multimodal Physiological Signals and Neuromorphic Computing Based on SNNs
Currently, the integration of artificial intelligence (AI) techniques with multimodal
physiological signals represents a pivotal approach to detect affective disorders (ADs). With …
physiological signals represents a pivotal approach to detect affective disorders (ADs). With …
Image-based facial emotion recognition using convolutional neural network on emognition dataset
ES Agung, AP Rifai, T Wijayanto - Scientific Reports, 2024 - nature.com
Detecting emotions from facial images is difficult because facial expressions can vary
significantly. Previous research on using deep learning models to classify emotions from …
significantly. Previous research on using deep learning models to classify emotions from …
Design of subject independent 3D VAD emotion detection system using EEG signals and machine learning algorithms
This work aims to develop a subject-independent Emotion Detection System (EDS) based
on EEG signals and the 3D Valence-Arousal-Dominance (VAD) model. The DEAP database …
on EEG signals and the 3D Valence-Arousal-Dominance (VAD) model. The DEAP database …
A multi-modal driver emotion dataset and study: Including facial expressions and synchronized physiological signals
G Xiang, S Yao, H Deng, X Wu, X Wang, Q Xu… - … Applications of Artificial …, 2024 - Elsevier
To address the limitations of databases in the field of emotion recognition and to cater to the
trend of integrating data from multiple sources, we have established a multi-modal emotional …
trend of integrating data from multiple sources, we have established a multi-modal emotional …
Emotion rendering for conversational speech synthesis with heterogeneous graph-based context modeling
Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the
appropriate prosody and emotional inflection within a conversational setting. While …
appropriate prosody and emotional inflection within a conversational setting. While …
TimelyTale: a multimodal dataset approach to assessing passengers' explanation demands in highly automated vehicles
Explanations in automated vehicles enhance passengers' understanding of vehicle decision-
making, mitigating negative experiences by increasing their sense of control. These …
making, mitigating negative experiences by increasing their sense of control. These …
Pathos in Natural Language Argumentation: Emotional Appeals and Reactions
B Konat, E Gajewska, W Rossa - Argumentation, 2024 - Springer
In this paper, we present a model of pathos, delineate its operationalisation, and
demonstrate its utility through an analysis of natural language argumentation. We …
demonstrate its utility through an analysis of natural language argumentation. We …
A Multimodal Dataset for Mixed Emotion Recognition
Mixed emotions have attracted increasing interest recently, but existing datasets rarely focus
on mixed emotion recognition from multimodal signals, hindering the affective computing of …
on mixed emotion recognition from multimodal signals, hindering the affective computing of …