ArticleSensors (Basel, Switzerland)2019
Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning approaches to anxiety detection: trends, model evaluation, and future directions.Frontiers in artificial intelligence · 2025Pooled it
- New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- A Comparison of Self-Reports and Unobtrusive Multimodal Measures of Stress in a Realistic Simulated Task Environment.Sensors (Basel, Switzerland) · 2026Article
- AI in Mental Health: Transforming Diagnosis and Management of Depression and Anxiety.Health science reports · 2026Article
- Recognizing anxiety and depression in cancer patients based on speech and facial expressions.Frontiers in psychiatry · 2026Article
- A novel deep learning model for objective quantification of generalized anxiety disorder severity using EEG functional connectivity.Frontiers in psychiatry · 2026Article
- A multimodal stress detection dataset with facial expressions and physiological signals.Scientific data · 2025Article
- Comparative Efficacy of MultiModal AI Methods in Screening for Major Depressive Disorder: Machine Learning Model Development Predictive Pilot Study.JMIR formative research · 2025Article
- A novel approach to depression detection using POV glasses and machine learning for multimodal analysis.Frontiers in psychiatry · 2025Article
- Predicting vasovagal reactions to needles from video data using 2D-CNN with GRU and LSTM.PloS one · 2025Article
- Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned.Frontiers in psychiatry · 2025Article
- Facing Change: Using Automated Facial Expression Analysis to Examine Emotional Flexibility in the Treatment of Depression.Administration and policy in mental health · 2024Observational
- Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023.Heliyon · 2024Review
- Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.Journal of imaging · 2024Review
- Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors.Healthcare (Basel, Switzerland) · 2024Article
- Feasibility of virtual reality and machine learning to assess personality traits in an organizational environment.Frontiers in psychology · 2024Article
- Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion: Drawing Insights From Psychology, Engineering, and the Arts, This Article Provides a Comprehensive Overview of the Field of Emotion Analysis in Visual Media and Discusses the Latest Research, Systems, Challenges, Ethical Implications, and Potential Impact of Artificial Emotional Intelligence on Society.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2023Article
- Systematic review of machine learning in PTSD studies for automated diagnosis evaluation.Npj mental health research · 2023Review
- Facial Expressions Track Depressive Symptoms in Old Age.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present the first study in the literature that has aimed to determine Depression Anxiety Stress Scale (DASS) levels by analyzing facial expressions using Facial Action Coding System (FACS) by means of a unique noninvasive architecture on three layers designed to offer high accuracy and fast convergence: in the first layer, Active Appearance Models (AAM) and a set of multiclass Support Vector Machines (SVM) are used for Action Unit (AU) classification; in the second layer, a matrix is built containing the AUs' intensity levels; and in the third layer, an optimal feedforward neural network (FFNN) analyzes the matrix from the second layer in a pattern recognition task, predicting the DASS levels. We obtained 87.2% accuracy for depression, 77.9% for anxiety, and 90.2% for stress. The average prediction time was 64 s, and the architecture could be used in real time, allowing health practitioners to evaluate the evolution of DASS levels over time. The architecture could discriminate with 93% accuracy between healthy subjects and those affected by Major Depressive Disorder (MDD) or Post-traumatic Stress Disorder (PTSD), and 85% for Generalized Anxiety Disorder (GAD). For the first time in the literature, we determined a set of correlations between DASS, induced emotions, and FACS, which led to an increase in accuracy of 5%. When tested on AVEC 2014 and ANUStressDB, the method offered 5% higher accuracy, sensitivity, and specificity compared to other state-of-the-art methods.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.