mlsanityData to 7 Oct 2026

Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey

arXiv 2403.08824 ↗First posted 9 Mar 2024cs.HC cs.AI cs.MMcited by 4

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Abstract (arXiv)

Analysis of stress, depression and engagement is less common and more complex than that of frequently discussed emotions such as happiness, sadness, fear and anger. The importance of these psychological states has been increasingly recognized due to their implications for mental health and well-being. Stress and depression are interrelated and together they impact engagement in daily tasks, highlighting the need to explore their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression and engagement. We present a taxonomy and timeline of the computational approaches used to analyze them and we discuss the most commonly used datasets and input modalities, along with the categories and generic pipeline of these approaches. Subsequently, we describe state-of-the-art computational approaches, including a performance summary on the most commonly used datasets. Following this, we explore the applications of stress, depression and engagement analysis, along with the associated challenges, limitations and future research directions.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 30 of its 249 references that are in the map. First posted on arXiv 9 Mar 2024.

Builds on

30 of 249 references.

References · 30

  1. 2020Self-supervised ECG Representation Learning for Emotion Recognitioncited by 99
  2. 2019AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognitioncited by 87
  3. 2019Dreaddit: A Reddit Dataset for Stress Analysis in Social Mediacited by 81
  4. 2016DAiSEE: Towards User Engagement Recognition in the Wildcited by 39
  5. 2020MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysiscited by 38
  6. 2022Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Modelcited by 37
  7. 2018Prediction and Localization of Student Engagement in the Wildcited by 33
  8. 2023Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal Contrastcited by 33
  9. 2021Multimodal Engagement Analysis from Facial Videos in the Classroomcited by 27
  10. 2021The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stresscited by 27
  11. 2021Deep Learning for Depression Recognition with Audiovisual Cues: A Reviewcited by 24
  12. 2022The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stresscited by 23
18 more
  1. 2021The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvementscited by 22
  2. 2021Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Networkcited by 22
  3. 2019MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speechcited by 18
  4. 2023Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselinescited by 17
  5. 2023Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognitioncited by 17
  6. 2017Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networkscited by 14
  7. 2021Affect-driven Ordinal Engagement Measurement from Videocited by 11
  8. 2021FedMood: Federated Learning on Mobile Health Data for Mood Detectioncited by 10
  9. 2021Two-stage Temporal Modelling Framework for Video-based Depression Recognition using Graph Representationcited by 10
  10. 2022Depression Recognition using Remote Photoplethysmography from Facial Videoscited by 10
  11. 2020Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviewscited by 8
  12. 2022Engagement Detection with Multi-Task Training in E-Learning Environmentscited by 8
  13. 2021MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensorcited by 7
  14. 2019Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regressioncited by 5
  15. 2024Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detectioncited by 5
  16. 2022Automatic Context-Driven Inference of Engagement in HMI: A Surveycited by 4
  17. 2019DASPS: A Database for Anxious States based on a Psychological Stimulationcited by 2
  18. 2022Interpretable Multimodal Emotion Recognition using Hybrid Fusion of Speech and Image Datacited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 3.

4 papers.

  1. 2024TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signalscited by 7
  2. 2026PriorNet: Prior-Guided Engagement Estimation from Face Videocited by 1
  3. 2026GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videoscited by 0
  4. 2026Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognitioncited by 0