Eye movement analysis for depression detection

2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013

Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning. The absence of any objective diagnostic aid for depression leads to a range of subjective biases in initial diagnosis and ongoing monitoring. Psychologists use various visual cues in their assessment to quantify depression such as facial expressions, eye contact and head movements. This paper studies the contribution of (upper) body expressions and gestures for automatic depression analysis. A framework based on space-time interest points and bag of words is proposed for the analysis of upper body and facial movements. Salient interest points are selected using clustering. The major contribution of this paper lies in the creation of a bag of body expressions and a bag of facial dynamics for assessing the contribution of different body parts for depression analysis. Head movement analysis is performed by selecting rigid facial fiducial points and a new histogram of head movements is proposed. The experiments are performed on real-world clinical data where video clips of patients and healthy controls are recorded during interactive interview sessions. The results show the effectiveness of the proposed system to evaluate the contribution of various body parts in depression analysis.

2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009

This paper is motivated by Ellgring's work in non-verbal communication in depression to measure and compare the levels of facial activity, before and after treatment, of endogenous and neurotic depressives. Similar to that work, we loosely associate the measurements with Action Units (AU) groups from the Facial Action Coding System (FACS). However, we use the neologism Region Units (RU) to describe regions of the face that encapsulate AUs. In contrast to Ellgring's approach, we automatically generate the measurements and provide both prototypical expression recognition and RU-specific activity measurements. Latency between expressions is also measured and the system is conducive to comparison across groups and individual subjects. By using Active Appearance Models (AAM) to locate the fiduciary facial points, and MultiBoost to classify prototypical expressions and the RUs, we can provide a simple, objective, flexible and cost-effective means of automatically measuring facial activity.

International Journal of Marketing, Communication and New Media, 2024

Well-being is a dynamic construct that evolves over time and fluctuates within individuals, presenting challenges for accurate quantification. Reduced well-being is often linked to depression or anxiety disorders, which are characterised by biases in visual attention towards specific stimuli, such as human faces. This paper introduces a novel approach to AI-assisted screening of affective disorders by analysing visual attention scan paths using convolutional neural networks (CNNs). Data were collected from two studies examining (1) attentional tendencies in individuals diagnosed with major depression and (2) social anxiety. These data were processed using residual CNNs through images generated from eye-gaze patterns. Experimental results, obtained with ResNet architectures, demonstrated an average accuracy of 48% for a three-class system and 62% for a two-class system. Based on these exploratory findings, we propose that this method could be employed in rapid, ecological, and effective mental health screening systems to assess well-being through eye-tracking.

IEEE Transactions on Affective Computing, 2016

An estimated 350 million people worldwide are affected by depression. Using affective sensing technology, our longterm goal is to develop an objective multimodal system that augments clinical opinion during the diagnosis and monitoring of clinical depression. This paper steps towards developing a classification system-oriented approach, where feature selection, classification and fusion-based experiments are conducted to infer which types of behaviour (verbal and nonverbal) and behaviour combinations can best discriminate between depression and non-depression. Using statistical features extracted from speaking behaviour, eye activity, and head pose, we characterise the behaviour associated with major depression and examine the performance of the classification of individual modalities and when fused. Using a real-world, clinically validated dataset of 30 severely depressed patients and 30 healthy control subjects, a Support Vector Machine is used for classification with several feature selection techniques. Given the statistical nature of the extracted features, feature selection based on T-tests performed better than other methods. Individual modality classification results were considerably higher than chance level (83% for speech, 73% for eye, and 63% for head). Fusing all modalities shows a remarkable improvement compared to unimodal systems, which demonstrates the complementary nature of the modalities. Among the different fusion approaches used here, feature fusion performed best with up to 88% average accuracy. We believe that is due to the compatible nature of the extracted statistical features. Index Terms-Depression detection, multimodal fusion, speaking behaviour, eye activity, head pose ! 1 INTRODUCTION F LUCTUATIONS in mood are a normal part of most people's emotional lives, as long as such fluctuations are not severe, frequent, or interfere with that individual's daily and social life functions. If they do, a psychiatric disorder such as major depression disorder might be present. Major depression is a mood disorder that may last for weeks, months, even years, vary in severity, and is associated with distress and disability that impair an individual's ability to function in daily life. The World Health Organisation (WHO) lists depression as the fourth most significant cause of disability worldwide and predicts it to be the leading

International Journal of Advance Research, Ideas and Innovations in Technology, 2020

Depression is one of the most serious problems faced by today’s world. At the same time, it is the most difficult mental disorder to detect. Symptoms of depression vary from person to person. So it’s difficult for a doctor to diagnose and treat it. And most of the methods to diagnose depression depend on an interactive session with the patient or on the basis of behavioral observation. It is observed that most of the time patients don’t give correct answers to the question asked or tries to behave normally in front of the doctor, making it difficult for a doctor to diagnose depression. To overcome all these difficulties we have developed an automatic depression analysis method that uses dynamic facial landmark descriptor. This method gives the Depression score, on the bases of which we decide a person is depressed or not. As it does not involve any human interference such as clinical interview, doctor’s interpretation, it gives accurate results.

Brain Sciences

Affective state estimation is a research field that has gained increased attention from the research community in the last decade. Two of the main catalysts for this are the advancement in the data analysis using artificial intelligence and the availability of high-quality video. Unfortunately, benchmarks and public datasets are limited, thus making the development of new methodologies and the implementation of comparative studies essential. The current work presents the eSEE-d database, which is a resource to be used for emotional State Estimation based on Eye-tracking data. Eye movements of 48 participants were recorded as they watched 10 emotion-evoking videos, each of them followed by a neutral video. Participants rated four emotions (tenderness, anger, disgust, sadness) on a scale from 0 to 10, which was later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled three self-assessment questionnaires. An extensive analysis of the parti...

Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge - AVEC '13, 2013

Quantifying behavioural changes in depression using affective computing techniques is the first step in developing an objective diagnostic aid, with clinical utility, for clinical depression. As part of the AVEC 2013 Challenge, we present a multimodal approach for the Depression Sub-Challenge using a GMM-UBM system with three different kernels for the audio subsystem and Space Time Interest Points in a Bag-of-Words approach for the vision subsystem. These are then fused at the feature level to form the combined AV system. Key results include the strong performance of acoustic audio features and the bag-of-words visual features in predicting an individual's level of depression using regression. Interestingly, in the context of the small amount of literature on the subject, is that our feature level multimodal fusion technique is able to outperform both the audio and visual challenge baselines.