StressID is a Dataset specifically designed for stress identification based on multimodal data collection.

StressID contains video, audio and physiological signals.

It is contains RGB facial video, audio and physiological signals (ECG, EDA, Respiration). Different stress-inducing stimuli are used: emotional video-clips, cognitive tasks and public speaking. The total dataset is consists of recordings from 65 participants that performed 11 tasks. Each task is labeled by the subjects in terms of stress, relaxation, arousal, and valence. The experimental set-up ensures synchronised, high-quality, and low noise data. Code for unimodal and multimodal baseline models is available at

StressID data and set-up.


We thank all the 65 participant. Without them this dataset and the related research would have never been possible.

We thanks our institutions, INRIA and EURECOM, and the French government that supported us through the following programs: the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR) (ANR-19-P3IA-0002), the ANR RESPECT Project (ANR-18-CE92-0024), the UCAJEDI Investments in the Future project managed by Ville de Nice and ANR (ANR-15-IDEX-01) and by PNRR- Investment 1.5 Ecosystems of Innovation, Project Tuscany Health Ecosystem (THE), Spoke
3 “Advanced technologies, methods, materials and heath analytics” CUP: I53C22000780001.


title={Stress{ID}: a Multimodal Dataset for Stress Identification},
author={Hava Chaptoukaev and Valeriya Strizhkova and Michele Panariello and Bianca Dalpaos and Aglind Reka and Valeria Manera and Susanne Thummler and Esma ISMAILOVA and Nicholas W. and Francois Bremond and Massimiliano Todisco and Maria A Zuluaga and Laura M. Ferrari},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},

The poster and the full paper is available at

More technical information can be found here


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