PTSD-MDNN : Fusion tardive de réseaux de neurones profonds multimodaux pour la détection du trouble de stress post-traumatique

Fuente: arXiv
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Auteurs principaux: Nguyen-Phuoc, Long, Gaboriau, Renald, Delacroix, Dimitri, Navarro, Laurent
Format: Preprint
Publié: 2024
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author Nguyen-Phuoc, Long
Gaboriau, Renald
Delacroix, Dimitri
Navarro, Laurent
author_facet Nguyen-Phuoc, Long
Gaboriau, Renald
Delacroix, Dimitri
Navarro, Laurent
contents In order to provide a more objective and quicker way to diagnose post-traumatic stress disorder (PTSD), we present PTSD-MDNN which merges two unimodal convolutional neural networks and which gives low detection error rate. By taking only videos and audios as inputs, the model could be used in the configuration of teleconsultation sessions, in the optimization of patient journeys or for human-robot interaction.
format Preprint
id arxiv_https___arxiv_org_abs_2403_10565
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle PTSD-MDNN : Fusion tardive de réseaux de neurones profonds multimodaux pour la détection du trouble de stress post-traumatique
Nguyen-Phuoc, Long
Gaboriau, Renald
Delacroix, Dimitri
Navarro, Laurent
Audio and Speech Processing
Artificial Intelligence
Computer Vision and Pattern Recognition
Sound
Image and Video Processing
Neurons and Cognition
In order to provide a more objective and quicker way to diagnose post-traumatic stress disorder (PTSD), we present PTSD-MDNN which merges two unimodal convolutional neural networks and which gives low detection error rate. By taking only videos and audios as inputs, the model could be used in the configuration of teleconsultation sessions, in the optimization of patient journeys or for human-robot interaction.
title PTSD-MDNN : Fusion tardive de réseaux de neurones profonds multimodaux pour la détection du trouble de stress post-traumatique
topic Audio and Speech Processing
Artificial Intelligence
Computer Vision and Pattern Recognition
Sound
Image and Video Processing
Neurons and Cognition
url https://arxiv.org/abs/2403.10565