Exploring Facial Biomarkers for Depression through Temporal Analysis of Action Units

• URL: http://arxiv.org/abs/2407.13753v1
• Date: Thu, 18 Jul 2024 17:55:01 GMT
• Title: Exploring Facial Biomarkers for Depression through Temporal Analysis of Action Units
• Authors: Aditya Parikh, Misha Sadeghi, Bjorn Eskofier,
• Abstract summary: We analyzed facial expressions from video data of participants classified with or without depression. Results indicate significant differences in the intensities of AUs associated with sadness and happiness between the groups.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Depression is characterized by persistent sadness and loss of interest, significantly impairing daily functioning and now a widespread mental disorder. Traditional diagnostic methods rely on subjective assessments, necessitating objective approaches for accurate diagnosis. Our study investigates the use of facial action units (AUs) and emotions as biomarkers for depression. We analyzed facial expressions from video data of participants classified with or without depression. Our methodology involved detailed feature extraction, mean intensity comparisons of key AUs, and the application of time series classification models. Furthermore, we employed Principal Component Analysis (PCA) and various clustering algorithms to explore the variability in emotional expression patterns. Results indicate significant differences in the intensities of AUs associated with sadness and happiness between the groups, highlighting the potential of facial analysis in depression assessment.

Related papers

• Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants [2.2909783327197393]
  We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population. Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app. We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate.
  arXiv  Detail & Related papers  (2024-09-24T16:05:17Z)
• Neural Modulation Alteration to Positive and Negative Emotions in Depressed Patients: Insights from fMRI Using Positive/Negative Emotion Atlas [14.281212655952105]
  FMRI is a cutting-edge medical imaging technology renowned for its high spatial resolution and dynamic temporal information. We created positive emotion atlas (PEA) and negative emotion atlas (NEA) We examined changes in depression patients using these atlases and evaluated their diagnostic performance based on machine learning.
  arXiv  Detail & Related papers  (2024-07-26T03:52:08Z)
• Faces of the Mind: Unveiling Mental Health States Through Facial Expressions in 11,427 Adolescents [12.51443153354506]
  Mood disorders, including depression and anxiety, often manifest through facial expressions. We analyzed facial videos of 11,427 participants, a dataset two orders of magnitude larger than previous studies.
  arXiv  Detail & Related papers  (2024-05-30T14:02:40Z)
• Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study [0.6524460254566905]
  Depression has affected millions of people worldwide and has become one of the most common mental disorders. Recent research has evidenced that machine learning (ML) and Natural Language Processing (NLP) tools and techniques have significantly been used to diagnose depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present.
  arXiv  Detail & Related papers  (2024-04-03T19:45:40Z)
• Measuring Non-Typical Emotions for Mental Health: A Survey of Computational Approaches [57.486040830365646]
  Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement.
  arXiv  Detail & Related papers  (2024-03-09T11:16:09Z)
• The Relationship Between Speech Features Changes When You Get Depressed: Feature Correlations for Improving Speed and Performance of Depression Detection [69.88072583383085]
  This work shows that depression changes the correlation between features extracted from speech. Using such an insight can improve the training speed and performance of depression detectors based on SVMs and LSTMs.
  arXiv  Detail & Related papers  (2023-07-06T09:54:35Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• Depression Recognition using Remote Photoplethysmography from Facial Videos [0.3867363075280544]
  Depression is a mental illness that may be harmful to an individual's health. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) We propose a novel scheme that directly extracts them from facial videos, just based on visual information, removing the need for any contact-based device.
  arXiv  Detail & Related papers  (2022-06-09T10:23:49Z)
• Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction [53.03469655641418]
  We present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition. We establish the state-of-the-art performance of non-contact pain recognition on publicly available pain databases.
  arXiv  Detail & Related papers  (2021-05-18T20:47:45Z)
• Emotion pattern detection on facial videos using functional statistics [62.997667081978825]
  We propose a technique based on Functional ANOVA to extract significant patterns of face muscles movements. We determine if there are time-related differences on expressions among emotional groups by using a functional F-test.
  arXiv  Detail & Related papers  (2021-03-01T08:31:08Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.