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We present MDEAW, a multimodal database consisting of Electrodermal Activity (EDA) and Photoplethysmography (PPG) signals recorded during the exams for the course taught by the teacher at Eurecat Academy, Sabadell, Barcelona in order to…
In the field of affective computing, researchers in the community have promoted the performance of models and algorithms by using the complementarity of multimodal information. However, the emergence of more and more modal information makes…
Estimating Emotional Mimicry Intensity (EMI) in naturalistic environments is a critical yet challenging task in affective computing. The primary difficulty lies in effectively modeling the complex, nonlinear temporal dynamics across highly…
Previous studies have demonstrated that emotional features from a single acoustic sentiment label can enhance depression diagnosis accuracy. Additionally, according to the Emotion Context-Insensitivity theory and our pilot study,…
Recognizing emotions in spoken communication is crucial for advanced human-machine interaction. Current emotion detection methodologies often display biases when applied cross-corpus. To address this, our study amalgamates 16 diverse…
The research on human emotion under multimedia stimulation based on physiological signals is an emerging field, and important progress has been achieved for emotion recognition based on multi-modal signals. However, it is challenging to…
This paper presents a novel Electrodermal Activity (EDA) signal acquisition system, designed to address the challenges of stress monitoring in contemporary society, where stress affects one in four individuals. Our system focuses on…
Stress is various mental health disorders including depression and anxiety among college students. Early stress diagnosis and intervention may lower the risk of developing mental illnesses. We examined a machine learning-based method for…
Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design an automated model that can assess emotional states. Lately,…
Different from the emotion recognition in individual utterances, we propose a multimodal learning framework using relation and dependencies among the utterances for conversational emotion analysis. The attention mechanism is applied to the…
Inspired by the human ability to infer emotions from body language, we propose an automated framework for body language based emotion recognition starting from regular RGB videos. In collaboration with psychologists, we further extend the…
Automatic emotion recognition has become increasingly important with the rise of AI, especially in fields like healthcare, education, and automotive systems. However, there is a lack of multimodal datasets, particularly involving body…
Video-based Emotional Reaction Intensity (ERI) estimation measures the intensity of subjects' reactions to stimuli along several emotional dimensions from videos of the subject as they view the stimuli. We propose a multi-modal architecture…
We used two multimodal models for continuous valence-arousal recognition using visual, audio, and linguistic information. The first model is the same as we used in ABAW2 and ABAW3, which employs the leader-follower attention. The second…
Emotion AI is the ability of computers to understand human emotional states. Existing works have achieved promising progress, but two limitations remain to be solved: 1) Previous studies have been more focused on short sequential video…
Although the terms mood and emotion are closely related and often used interchangeably, they are distinguished based on their duration, intensity and attribution. To date, hardly any computational models have (a) examined mood recognition,…
This study aims to identify a set of indicators to estimate cognitive workload using a multimodal sensing approach and machine learning. A set of three cognitive tests were conducted to induce cognitive workload in twelve participants at…
This paper proposes a novel 3D speech-to-animation (STA) generation framework designed to address the shortcomings of existing models in producing diverse and emotionally resonant animations. Current STA models often generate animations…
Accurately detecting drowsiness is vital to driving safety. Among all measures, physiological-signal-based drowsiness monitoring can be more privacy-preserving than a camera-based approach. However, conflicts exist regarding how…
Multimodal Sentiment Analysis (MSA) stands as a critical research frontier, seeking to comprehensively unravel human emotions by amalgamating text, audio, and visual data. Yet, discerning subtle emotional nuances within audio and video…