# PTSD in the Wild: A Video Database for Studying Post-Traumatic Stress Disorder Recognition in Unconstrained Environments

arXiv: https://arxiv.org/abs/2209.14085 · HTML: https://mlsanity.com/paper/2209.14085/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Effectiveness of self-supervised pre-training for speech recognition](https://mlsanity.com/paper/1911.03912/) (2019)
- [Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2103.09154/) (2021)
- [The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements](https://mlsanity.com/paper/2101.06053/) (2021)
- [MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech](https://mlsanity.com/paper/1909.07208/) (2019)
- [AudVowelConsNet: A Phoneme-Level Based Deep CNN Architecture for Clinical Depression Diagnosis](https://mlsanity.com/paper/2010.16201/) (2020)

## Built on by (most cited first, 6 of 6)

- [Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges](https://mlsanity.com/paper/2407.16804/) (2024)
- [Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding](https://mlsanity.com/paper/2510.04899/) (2025)
- [Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization](https://mlsanity.com/paper/2501.16106/) (2025)
- [OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization](https://mlsanity.com/paper/2602.10635/) (2026)
- [PTSD-MDNN : Fusion tardive de r'eseaux de neurones profonds multimodaux pour la d'etection du trouble de stress post-traumatique](https://mlsanity.com/paper/2403.10565/) (2024)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)

## Abstract (arXiv)

POST-traumatic stress disorder (PTSD) is a chronic and debilitating mental condition that is developed in response to catastrophic life events, such as military combat, sexual assault, and natural disasters. PTSD is characterized by flashbacks of past traumatic events, intrusive thoughts, nightmares, hypervigilance, and sleep disturbance, all of which affect a person's life and lead to considerable social, occupational, and interpersonal dysfunction. The diagnosis of PTSD is done by medical professionals using self-assessment questionnaire of PTSD symptoms as defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM). In this paper, and for the first time, we collected, annotated, and prepared for public distribution a new video database for automatic PTSD diagnosis, called PTSD in the wild dataset. The database exhibits "natural" and big variability in acquisition conditions with different pose, facial expression, lighting, focus, resolution, age, gender, race, occlusions and background. In addition to describing the details of the dataset collection, we provide a benchmark for evaluating computer vision and machine learning based approaches on PTSD in the wild dataset. In addition, we propose and we evaluate a deep learning based approach for PTSD detection in respect to the given benchmark. The proposed approach shows very promising results. Interested researcher can download a copy of PTSD-in-the wild dataset from: http://www.lissi.fr/PTSD-Dataset/