The First MPDD Challenge: Multimodal Personality-aware Depression Detection

• URL: http://arxiv.org/abs/2505.10034v3
• Date: Thu, 29 May 2025 02:12:21 GMT
• Title: The First MPDD Challenge: Multimodal Personality-aware Depression Detection
• Authors: Changzeng Fu, Zelin Fu, Qi Zhang, Xinhe Kuang, Jiacheng Dong, Kaifeng Su, Yikai Su, Wenbo Shi, Junfeng Yao, Yuliang Zhao, Shiqi Zhao, Jiadong Wang, Siyang Song, Chaoran Liu, Yuichiro Yoshikawa, Björn Schuller, Hiroshi Ishiguro,
• Abstract summary: Depression is a widespread mental health issue affecting diverse age groups.<n>Current approaches often establish a direct mapping between multimodal data and depression indicators.<n>The MPDD Challenge aims to address this gap by incorporating multimodal data alongside individual difference factors.
• Score: 15.976782664827315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depression is a widespread mental health issue affecting diverse age groups, with notable prevalence among college students and the elderly. However, existing datasets and detection methods primarily focus on young adults, neglecting the broader age spectrum and individual differences that influence depression manifestation. Current approaches often establish a direct mapping between multimodal data and depression indicators, failing to capture the complexity and diversity of depression across individuals. This challenge includes two tracks based on age-specific subsets: Track 1 uses the MPDD-Elderly dataset for detecting depression in older adults, and Track 2 uses the MPDD-Young dataset for detecting depression in younger participants. The Multimodal Personality-aware Depression Detection (MPDD) Challenge aims to address this gap by incorporating multimodal data alongside individual difference factors. We provide a baseline model that fuses audio and video modalities with individual difference information to detect depression manifestations in diverse populations. This challenge aims to promote the development of more personalized and accurate de pression detection methods, advancing mental health research and fostering inclusive detection systems. More details are available on the official challenge website: https://hacilab.github.io/MPDDChallenge.github.io.

Related papers

• Speech as a Multimodal Digital Phenotype for Multi-Task LLM-based Mental Health Prediction [0.4517077427559345]
  We propose the treatment of patient speech data as a trimodal multimedia data source for depression detection.<n>Our proposed approach, featuring trimodal, longitudinal MTL is evaluated on the Depression Early Warning dataset.<n>It achieves a balanced accuracy of 70.8%, which is higher than each of the unimodal, single-task, and non-longitudinal methods.
  arXiv  Detail & Related papers  (2025-05-28T04:07:17Z)
• 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)
• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• LMVD: A Large-Scale Multimodal Vlog Dataset for Depression Detection in the Wild [35.64843242574305]
  Large-scale multimodal vlog dataset (LMVD) for depression recognition in the wild is built. A novel architecture termed MDDformer to learn the non-verbal behaviors of individuals is proposed.
  arXiv  Detail & Related papers  (2024-05-09T01:27:10Z)
• Multimodal Fusion on Low-quality Data: A Comprehensive Survey [110.22752954128738]
  This paper surveys the common challenges and recent advances of multimodal fusion in the wild. We identify four main challenges that are faced by multimodal fusion on low-quality data. This new taxonomy will enable researchers to understand the state of the field and identify several potential directions.
  arXiv  Detail & Related papers  (2024-04-27T07:22:28Z)
• Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks [53.257804915263165]
  Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. Key evidence from neuroimaging data for pathological commonness remains unrevealed. We build a deep learning model, using multi-site functional magnetic resonance imaging data, for classifying 5 different brain disorders from healthy controls.
  arXiv  Detail & Related papers  (2023-02-23T09:22:05Z)
• 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)
• Subgroup discovery of Parkinson's Disease by utilizing a multi-modal smart device system [63.20765930558542]
  We used smartwatches and smartphones to collect multi-modal data from 504 participants, including PD patients, DD and HC. We were able to show that by combining various modalities, classification accuracy improved and further PD clusters were discovered.
  arXiv  Detail & Related papers  (2022-05-12T08:59:57Z)
• Data set creation and empirical analysis for detecting signs of depression from social media postings [0.0]
  Depression is a common mental illness that has to be detected and treated at an early stage to avoid serious consequences. We developed a gold standard data set that detects the levels of depression as not depressed', moderately depressed' and severely depressed' from the social media postings.
  arXiv  Detail & Related papers  (2022-02-07T10:24:33Z)
• Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling [74.83957286553924]
  We infer the Myers-Briggs Personality Type indicators by applying a novel multi-view fusion framework, called "PERS" Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources.
  arXiv  Detail & Related papers  (2021-06-20T10:48:49Z)
• 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)
• Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies [0.0]
  Depression has been a leading cause of mental-health illnesses across the world. Using behavioural cues to automate depression diagnosis and stage prediction in recent years has relatively increased. The absence of labelled behavioural datasets and a vast amount of possible variations prove to be a major challenge in accomplishing the task.
  arXiv  Detail & Related papers  (2020-09-11T20:44:28Z)

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.