[HTML][HTML] A survey on databases for multimodal emotion recognition and an introduction to the VIRI (visible and InfraRed image) database

MFH Siddiqui, P Dhakal, X Yang, AY Javaid - Multimodal Technologies …, 2022‏ - mdpi.com
Multimodal human–computer interaction (HCI) systems pledge a more human–human-like
interaction between machines and humans. Their prowess in emanating an unambiguous …

A clinical and technical methodological review on stress detection and sleep quality prediction in an academic environment

J Medikonda - Computer methods and programs in biomedicine, 2023‏ - Elsevier
Background Mental health in recent times is a much talked about topic and its effects on the
sleep health of the students are said to result in long-term health issues if not identified and …

Stress detection through wrist-based electrodermal activity monitoring and machine learning

L Zhu, P Spachos, PC Ng, Y Yu, Y Wang… - IEEE Journal of …, 2023‏ - ieeexplore.ieee.org
Stress is an inevitable part of modern life. While stress can negatively impact a person's life
and health, positive and under-controlled stress can also enable people to generate creative …

Multimodal hierarchical CNN feature fusion for stress detection

R Kuttala, R Subramanian, VRM Oruganti - IEEE Access, 2023‏ - ieeexplore.ieee.org
Stress is one of the most severe concerns in modern life. High-level stress can create
various diseases or loss of focus and productivity at work. Being under stress prevents …

[HTML][HTML] Applying self-supervised representation learning for emotion recognition using physiological signals

KG Montero Quispe, DMS Utyiama, EM Dos Santos… - Sensors, 2022‏ - mdpi.com
The use of machine learning (ML) techniques in affective computing applications focuses on
improving the user experience in emotion recognition. The collection of input data (eg …

A systematic review of intermediate fusion in multimodal deep learning for biomedical applications

V Guarrasi, F Aksu, CM Caruso, F Di Feola… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Deep learning has revolutionized biomedical research by providing sophisticated methods
to handle complex, high-dimensional data. Multimodal deep learning (MDL) further …

A multimodal intermediate fusion network with manifold learning for stress detection

M Bodaghi, M Hosseini… - 2024 IEEE 3rd …, 2024‏ - ieeexplore.ieee.org
Multimodal deep learning methods capture synergistic features from multiple modalities and
have the potential to improve accuracy for stress detection compared to unimodal methods …

Mental stress classification based on a support vector machine and naive Bayes using electrocardiogram signals

M Kang, S Shin, G Zhang, J Jung, YT Kim - Sensors, 2021‏ - mdpi.com
Examining mental health is crucial for preventing mental illnesses such as depression. This
study presents a method for classifying electrocardiogram (ECG) data into four emotional …

Joint modality features in frequency domain for stress detection

K Radhika, R Subramanian, VRM Oruganti - IEEE Access, 2022‏ - ieeexplore.ieee.org
Rich feature extraction is essential to train a good machine learning (ML) framework. These
features are generally extracted separately from each modality. We hypothesize that richer …

Disaster assessment from social media using multimodal deep learning

NP Shetty, Y Bijalwan, P Chaudhari, J Shetty… - Multimedia Tools and …, 2024‏ - Springer
Real-time global event detection particularly catastrophic events has benefited significantly
due to the ubiquitous adoption of social media platforms and advancements in image …