Spectral Representation of Behaviour Primitives for Depression Analysis
| العنوان: | Spectral Representation of Behaviour Primitives for Depression Analysis |
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| المؤلفون: | Michel Valstar, Linlin Shen, Shashank Jaiswal, Siyang Song |
| بيانات النشر: | Institute of Electrical and Electronics Engineers, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | 021110 strategic, defence & security studies, Spectral representation, Point (typography), business.industry, Computer science, Frame (networking), 0211 other engineering and technologies, Context (language use), 02 engineering and technology, Machine learning, computer.software_genre, Diagnostic tools, Beacon - Smart Products, Biomedical Research Centre, Human-Computer Interaction, Task (computing), Dynamics (music), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Depression (differential diagnoses), Software, Computing & Mathematics - Artificial intelligence |
| الوصف: | Depression is a serious mental disorder affecting millions of people. Traditional clinical diagnosis methods are subjective, complicated and require extensive participation of clinicians. Recent advances in automatic depression analysis systems promise a future where these shortcomings are addressed by objective, repeatable, and readily available diagnostic tools to aid health professionals in their work. Yet there remain a number of barriers to the development of such tools. One barrier is that existing automatic depression analysis algorithms base their predictions on very brief sequential segments, sometimes as little as one frame. Another barrier is that existing methods do not take into account what the context of the measured behaviour is. In this paper, we extract multi-scale video-level features for video-based automatic depression analysis. We propose to use automatically detected human behaviour primitives as the low-dimensional descriptor for each frame. We also propose two novel spectral representations to represent video-level multi-scale temporal dynamics of expressive behaviour. Constructed spectral representations are fed to CNNs and ANNs for depression analysis. In addition to achieving state-of-the-art accuracy in depression severity estimation, we show that the task conducted by the user matters, that fusion of a combination of tasks reaches highest accuracy, and that longer tasks are more informative than shorter tasks, up to a point. |
| وصف الملف: | |
| اللغة: | English |
| تدمد: | 1949-3045 2371-9850 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::392b7aa17c2e5ecf529e6ff6e9454c55 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....392b7aa17c2e5ecf529e6ff6e9454c55 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19493045 23719850 |
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