Szczegóły publikacji
Opis bibliograficzny
End-to-end multimodal system for depression detection from online recordings / Mateusz Kowalewski, Maciej Stroiński, Kamil Kwarciak, Volodymyr Laptiev, Daria HEMMERLING // W: EMBC 2023 [Dokument elektroniczny] : 2023 45th annual international conference of the IEEE Engineering in Medicine & Biology Conference : Sydney, Australia, 24-27 July 2023 : proceedings / IEEE. — Wersja do Windows. — Dane tekstowe. — [Piscataway, NJ, USA] : IEEE, cop. 2023. — (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; ISSN 1094-687X). — e-ISBN: 979-8-3503-2447-1. — S. [1-4]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [4], Abstr. — D. Hemmerling - dod. afiliacja: SoftServe, Wroclaw, Poland
Autorzy (5)
- Kowalewski Mateusz
- AGHStroiński Maciej
- AGHKwarciak Kamil
- Laptiev Volodymyr
- AGHHemmerling Daria
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150898 |
|---|---|
| Data dodania do BaDAP | 2024-01-05 |
| Tekst źródłowy | URL |
| DOI | 10.1109/EMBC40787.2023.10340782 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | The Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023 |
| Czasopismo/seria | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abstract
Depression is one of the most occurring civilizational diseases. In this paper, we propose a new approach for detecting depression through the analysis of social media content using face analysis, emotion recognition neural networks, and speech processing. We utilized audio-visual analysis and acquired more than 605 features in the time domain. Those are fed to machine learning and deep learning models for depression classification. Our approach outperforms the other state-of-the-art models, achieving the F1-score 0.77. The results have the potential to provide valuable insights for mental health professionals, offer early detection and intervention, and serve as a resource for individuals seeking help with their mental health. This study enables real-time analysis and represents a significant advancement in mental health and technology and has the potential to impact society.Clinical relevance—The system aims to provide a fast and accurate way to detect depression in individuals through online recordings. The use of multimodal information (e.g. audio, image) enhances the performance of the non-verbal behavioral analysis. The end-to-end system reduces the need for manual analysis by mental health professionals and increases the efficiency of depression screening. The system can potentially help identify individuals who are at risk for depression, enabling early intervention and treatment. The results from the system can complement traditional assessments and support mental health professionals in making a diagnosis. The system can be used in real-time processing, f.e. during online calls, and provide objective measurements summarizing the overall behavior based on computer vision and audio analysis.
