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Article

Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System

Department of Telecommunications, Faculty of Electronics, Telecommunications and Information Technology, University “Politehnica”, Bucharest 061071, Romania
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Sensors 2019, 19(17), 3693; https://doi.org/10.3390/s19173693
Received: 28 June 2019 / Revised: 11 August 2019 / Accepted: 20 August 2019 / Published: 25 August 2019
(This article belongs to the Special Issue Sensors for Affective Computing and Sentiment Analysis)
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. View Full-Text
Keywords: affective computing; stress prediction; depression prediction; anxiety prediction; neural networks; face analysis affective computing; stress prediction; depression prediction; anxiety prediction; neural networks; face analysis
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MDPI and ACS Style

Gavrilescu, M.; Vizireanu, N. Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System. Sensors 2019, 19, 3693. https://doi.org/10.3390/s19173693

AMA Style

Gavrilescu M, Vizireanu N. Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System. Sensors. 2019; 19(17):3693. https://doi.org/10.3390/s19173693

Chicago/Turabian Style

Gavrilescu, Mihai, and Nicolae Vizireanu. 2019. "Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System" Sensors 19, no. 17: 3693. https://doi.org/10.3390/s19173693

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