{"id":382,"date":"2023-06-20T17:24:42","date_gmt":"2023-06-20T15:24:42","guid":{"rendered":"https:\/\/project.inria.fr\/stressid\/?page_id=382"},"modified":"2025-02-19T15:28:21","modified_gmt":"2025-02-19T14:28:21","slug":"382-2","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/stressid\/","title":{"rendered":""},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<p>StressID is a Dataset specifically designed for <strong>stress identification<\/strong> based on <strong>multimodal data<\/strong> collection. <\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-columns are-vertically-aligned-center is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:60%\">\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<figure class=\"wp-block-video\"><video autoplay controls loop muted src=\"https:\/\/project.inria.fr\/stressid\/files\/2023\/06\/all4_-1.mp4\"><\/video><figcaption><em>StressID contains video, audio and physiological  signals.<\/em><\/figcaption><\/figure>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p>It is contains <strong>RGB facial video,<\/strong> <strong>audio<\/strong> and <strong>physiological signals (ECG, EDA, Respiration)<\/strong>. Different stress-inducing stimuli are used: emotional video-clips, cognitive tasks and public speaking. The total dataset is consists of recordings from <strong>65 participants<\/strong> that performed <strong>11 tasks<\/strong>. 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&nbsp;is available at <a href=\"https:\/\/github.com\/robustml-eurecom\/stressID\">https:\/\/github.com\/robustml-eurecom\/stressID<\/a>.<\/p>\n<\/div>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-large is-resized is-style-default\"><a href=\"https:\/\/project.inria.fr\/stressid\/files\/2023\/06\/StressID_IMG1-1.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/project.inria.fr\/stressid\/files\/2023\/06\/StressID_IMG1-1-1024x450.png\" alt=\"\" class=\"wp-image-96\" width=\"768\" height=\"380\"\/><\/a><figcaption>StressID data and set-up.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"has-medium-font-size wp-block-heading\">CREDITS<\/h2>\n\n\n\n<p>We thank all the 65 participant. Without them this dataset and the related research would have never been possible.<\/p>\n\n\n\n<p class=\"has-small-font-size\">We thanks our institutions, INRIA and EURECOM, and the French government that supported us through the following programs: the 3IA C\u00f4te d\u2019Azur 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<br>3 \u201cAdvanced technologies, methods, materials and heath analytics\u201d CUP: I53C22000780001.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"has-medium-font-size wp-block-heading\">CITATION<\/h2>\n\n\n\n<pre class=\"wp-block-code has-medium-font-size\"><code>@inproceedings{\nchaptoukaev2023stressid,\ntitle={Stress{ID}: a Multimodal Dataset for Stress Identification},\nauthor={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},\nbooktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\nyear={2023},\nurl={https:\/\/openreview.net\/forum?id=qWsQi9DGJb}\n}<\/code><\/pre>\n\n\n\n<p>The poster and the full paper is available at <a href=\"https:\/\/nips.cc\/virtual\/2023\/poster\/73454\">https:\/\/nips.cc\/virtual\/2023\/poster\/73454<\/a><\/p>\n\n\n\n<p>More technical information can be found <a href=\"https:\/\/project.inria.fr\/stressid\/files\/2023\/11\/Technical-info_final.pdf\" data-type=\"URL\">here<\/a><\/p>\n\n\n\n<h2 class=\"has-medium-font-size wp-block-heading\">HOW TO DOWNLOAD<\/h2>\n\n\n\n<p>Please download, read, fill up the form below, and send it to <a href=\"mailto:stressid.dataset@inria.fr\">stressid.dataset@inria.fr<\/a>. <\/p>\n\n\n\n<p><strong>Please note that only a person with a permanent position in academics can sign the license. <\/strong><\/p>\n\n\n\n<p>Please use your professional email address in the license and double-check it. Please attach to your request a link to a webpage that shows the affiliation and the professional email address.<\/p>\n\n\n\n<p>You are asked to insert your information at PAG.7, 8 and 10. <\/p>\n\n\n\n<p>The Dataset is licensed for non-commercial scientific research purposes. <\/p>\n\n\n\n<p>Any action that violates the license agreement is prosecutable by law.<\/p>\n\n\n\n<p>Once validated you will receive a mail with a secured link to download the dataset.<\/p>\n\n\n\n<p><a href=\"https:\/\/project.inria.fr\/stressid\/files\/2025\/02\/License_StressID_Academic_final.pdf\">LICENSE.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>StressID is a Dataset specifically designed for stress identification based on multimodal data collection. 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\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/stressid\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":2339,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-382","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/stressid\/wp-json\/wp\/v2\/pages\/382","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/stressid\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/stressid\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/stressid\/wp-json\/wp\/v2\/users\/2339"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/stressid\/wp-json\/wp\/v2\/comments?post=382"}],"version-history":[{"count":18,"href":"https:\/\/project.inria.fr\/stressid\/wp-json\/wp\/v2\/pages\/382\/revisions"}],"predecessor-version":[{"id":462,"href":"https:\/\/project.inria.fr\/stressid\/wp-json\/wp\/v2\/pages\/382\/revisions\/462"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/stressid\/wp-json\/wp\/v2\/media?parent=382"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}