HCI and Affective Health: Taking stock of a decade of studies and charting future research directions

P Sanches, A Janson, P Karpashevich… - Proceedings of the …, 2019 - dl.acm.org
In the last decade, the number of articles on HCI and health has increased dramatically. We
extracted 139 papers on depression, anxiety and bipolar health issues from 10 years of …

Automatic assessment of depression based on visual cues: A systematic review

A Pampouchidou, PG Simos, K Marias… - IEEE Transactions …, 2017 - ieeexplore.ieee.org
Automatic depression assessment based on visual cues is a rapidly growing research
domain. The present exhaustive review of existing approaches as reported in over sixty …

Dynamic multimodal measurement of depression severity using deep autoencoding

H Dibeklioğlu, Z Hammal… - IEEE journal of biomedical …, 2017 - ieeexplore.ieee.org
Depression is one of the most common psychiatric disorders worldwide, with over 350
million people affected. Current methods to screen for and assess depression depend …

End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis

M Muzammel, H Salam, A Othmani - Computer Methods and Programs in …, 2021 - Elsevier
Abstract Background and Objective: Major Depressive Disorder is a highly prevalent and
disabling mental health condition. Numerous studies explored multimodal fusion systems …

MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

T Chen, R Hong, Y Guo, S Hao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Major depressive disorder (MDD) is one of the most common and severe mental illnesses,
posing a huge burden on society and families. Recently, some multimodal methods have …

Emotion context insensitivity in depression: Toward an integrated and contextualized approach

LM Bylsma - Psychophysiology, 2021 - Wiley Online Library
Major depressive disorder (MDD) is characterized by pervasive mood disturbance as well as
deficits in emotional processing, reactivity, and regulation. There is accumulating evidence …

Audibert: A deep transfer learning multimodal classification framework for depression screening

E Toto, ML Tlachac, EA Rundensteiner - Proceedings of the 30th ACM …, 2021 - dl.acm.org
Depression is a leading cause of disability with tremendous socioeconomic costs. In spite of
early detection being crucial to improving prognosis, this mental illness remains largely …

Depression recognition using remote photoplethysmography from facial videos

CÁ Casado, ML Cañellas… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Depression is a mental illness that may be harmful to an individual's health. The detection of
mental health disorders in the early stages and a precise diagnosis are critical to avoid …

Human behaviour-based automatic depression analysis using hand-crafted statistics and deep learned spectral features

S Song, L Shen, M Valstar - 2018 13th IEEE international …, 2018 - ieeexplore.ieee.org
Depression is a serious mental disorder that affects millions of people all over the world.
Traditional clinical diagnosis methods are subjective, complicated and need extensive …

Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A …

S Yasin, A Othmani, I Raza, SA Hussain - Computers in Biology and …, 2023 - Elsevier
Mental disorders are rapidly increasing each year and have become a major challenge
affecting the social and financial well-being of individuals. There is a need for phenotypic …