# Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey arXiv: https://arxiv.org/abs/2403.08824 ยท HTML: https://mlsanity.com/paper/2403.08824/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 249 references that are in the map. - First posted on arXiv 9 Mar 2024. ## Builds on (most cited first, 30 of 30) - [Self-supervised ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020) - [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019) - [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019) - [DAiSEE: Towards User Engagement Recognition in the Wild](https://mlsanity.com/paper/1609.01885/) (2016) - [MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis](https://mlsanity.com/paper/2002.09283/) (2020) - [Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model](https://mlsanity.com/paper/2202.08210/) (2022) - [Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast](https://mlsanity.com/paper/2309.06924/) (2023) - [Prediction and Localization of Student Engagement in the Wild](https://mlsanity.com/paper/1804.00858/) (2018) - [Multimodal Engagement Analysis from Facial Videos in the Classroom](https://mlsanity.com/paper/2101.04215/) (2021) - [The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress](https://mlsanity.com/paper/2104.07123/) (2021) - [Deep Learning for Depression Recognition with Audiovisual Cues: A Review](https://mlsanity.com/paper/2106.00610/) (2021) - [The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress](https://mlsanity.com/paper/2207.05691/) (2022) - [The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements](https://mlsanity.com/paper/2101.06053/) (2021) - [Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Network](https://mlsanity.com/paper/2104.10122/) (2021) - [MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech](https://mlsanity.com/paper/1909.07208/) (2019) - [Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines](https://mlsanity.com/paper/2302.00431/) (2023) - [Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition](https://mlsanity.com/paper/2303.17611/) (2023) - [Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networks](https://mlsanity.com/paper/1705.07543/) (2017) - [Affect-driven Ordinal Engagement Measurement from Video](https://mlsanity.com/paper/2106.10882/) (2021) - [FedMood: Federated Learning on Mobile Health Data for Mood Detection](https://mlsanity.com/paper/2102.09342/) (2021) - [Two-stage Temporal Modelling Framework for Video-based Depression Recognition using Graph Representation](https://mlsanity.com/paper/2111.15266/) (2021) - [Depression Recognition using Remote Photoplethysmography from Facial Videos](https://mlsanity.com/paper/2206.04399/) (2022) - [Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews](https://mlsanity.com/paper/2006.08336/) (2020) - [Engagement Detection with Multi-Task Training in E-Learning Environments](https://mlsanity.com/paper/2204.04020/) (2022) - [MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensor](https://mlsanity.com/paper/2111.02561/) (2021) - [Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression](https://mlsanity.com/paper/1907.03422/) (2019) - [Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection](https://mlsanity.com/paper/2401.13327/) (2024) - [Automatic Context-Driven Inference of Engagement in HMI: A Survey](https://mlsanity.com/paper/2209.15370/) (2022) - [DASPS: A Database for Anxious States based on a Psychological Stimulation](https://mlsanity.com/paper/1901.02942/) (2019) - [Interpretable Multimodal Emotion Recognition using Hybrid Fusion of Speech and Image Data](https://mlsanity.com/paper/2208.11868/) (2022) ## Built on by (most cited first, 4 of 4) - [TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals](https://mlsanity.com/paper/2404.09474/) (2024) - [PriorNet: Prior-Guided Engagement Estimation from Face Video](https://mlsanity.com/paper/2605.03615/) (2026) - [GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videos](https://mlsanity.com/paper/2604.16214/) (2026) - [Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognition](https://mlsanity.com/paper/2605.04713/) (2026) ## 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.