doi:10.1016/j.cviu.2025.104619engGallego, NereaPlou, CarlosMarcos, MiguelUrcola, PabloMontesano, LuisMontijano, EduardoMartinez-Cantin, RubenMurillo, Ana C.EventSleep2: Sleep activity recognition on complete night sleep recordings with an event cameraART-2026-147979Sleep 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.2026http://zaguan.unizar.es/record/16853910.1016/j.cviu.2025.104619http://zaguan.unizar.es/record/168539oai:zaguan.unizar.es:168539info:eu-repo/grantAgreement/ES/DGA/T45-23Rinfo:eu-repo/grantAgreement/EC/H2020/ 101135782/EU/Trustworthy Efficient AI for Cloud-Edge Computing/MANOLOThis project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101135782-MANOLOinfo:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00info:eu-repo/grantAgreement/ES/MICINN/PID2021-125514NB-I00info:eu-repo/grantAgreement/ES/MICINN/PID2024–158322OB-I00info:eu-repo/grantAgreement/ES/MICINN/PID2024-159284NB-I00COMPUTER VISION AND IMAGE UNDERSTANDING 264 (2026), 104619 [13 pp.]by-nchttps://creativecommons.org/licenses/by-nc/4.0/deed.esinfo:eu-repo/semantics/openAccess