168539 20260209162330.0 doi 10.1016/j.cviu.2025.104619 sideral 147979 ART-2026-147979 eng Gallego, Nerea Universidad de Zaragoza (orcid)0009-0009-0819-0420 EventSleep2: Sleep activity recognition on complete night sleep recordings with an event camera 2026 Sleep is fundamental to health, and society is more and more aware of the impact and relevance of sleep disorders. Traditional diagnostic methods, like polysomnography, are intrusive and resource-intensive. Instead, research is focusing on developing novel, less intrusive or portable methods that combine intelligent sensors with activity recognition for diagnosis support and scoring. Event cameras offer a promising alternative for automated, in-home sleep activity recognition due to their excellent low-light performance and low power consumption. This work introduces EventSleep2-data, a significant extension to the EventSleep dataset, featuring 10 complete night recordings (around 7 h each) of volunteers sleeping in their homes. Unlike the original short and controlled recordings, this new dataset captures natural, full-night sleep sessions under realistic conditions. This new data incorporates challenging real-world scene variations, an efficient movement-triggered sparse data recording pipeline, and synchronized 2-channel EEG data for a subset of recordings. We also present EventSleep2-net, a novel event-based sleep activity recognition approach with a dual-head architecture to simultaneously analyze motion classes and static poses. The model is specifically designed to handle the motion-triggered, sparse nature of complete night recordings. Unlike the original EventSleep architecture, EventSleep2-net can predict both movement and static poses even during long periods with no events. We demonstrate state-of-the-art performance on both EventSleep1-data, the original dataset, and EventSleep2-data, with comprehensive ablation studies validating our design decisions. Together, EventSleep2-data and EventSleep2-net overcome the limitations of the previous setup and enable continuous, full-night analysis for real-world sleep monitoring, significantly advancing the potential of event-based vision for sleep disorder studies. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/DGA/T45-23R info:eu-repo/grantAgreement/EC/H2020/ 101135782/EU/Trustworthy Efficient AI for Cloud-Edge Computing/MANOLO This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101135782-MANOLO info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00 info:eu-repo/grantAgreement/ES/MICINN/PID2021-125514NB-I00 info:eu-repo/grantAgreement/ES/MICINN/PID2024–158322OB-I00 info:eu-repo/grantAgreement/ES/MICINN/PID2024-159284NB-I00 info:eu-repo/semantics/openAccess by-nc https://creativecommons.org/licenses/by-nc/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Plou, Carlos Universidad de Zaragoza Marcos, Miguel Universidad de Zaragoza Urcola, Pablo (orcid)0000-0003-3880-1842 Montesano, Luis Universidad de Zaragoza (orcid)0000-0003-1183-349X Montijano, Eduardo Universidad de Zaragoza (orcid)0000-0002-5176-3767 Martinez-Cantin, Ruben Universidad de Zaragoza (orcid)0000-0002-6741-844X Murillo, Ana C. Universidad de Zaragoza (orcid)0000-0002-7580-9037 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 264 (2026), 104619 [13 pp.] Comput. vis. image underst. COMPUTER VISION AND IMAGE UNDERSTANDING 1077-3142 2798001 http://zaguan.unizar.es/record/168539/files/texto_completo.pdf Versión publicada 2512281 http://zaguan.unizar.es/record/168539/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:168539 articulos driver 2026-02-09-14:43:01 ARTICLE