000168539 001__ 168539
000168539 005__ 20260209162330.0
000168539 0247_ $$2doi$$a10.1016/j.cviu.2025.104619
000168539 0248_ $$2sideral$$a147979
000168539 037__ $$aART-2026-147979
000168539 041__ $$aeng
000168539 100__ $$0(orcid)0009-0009-0819-0420$$aGallego, Nerea$$uUniversidad de Zaragoza
000168539 245__ $$aEventSleep2: Sleep activity recognition on complete night sleep recordings with an event camera
000168539 260__ $$c2026
000168539 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168539 5203_ $$aSleep 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.
000168539 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T45-23R$$9info:eu-repo/grantAgreement/EC/H2020/ 101135782/EU/Trustworthy Efficient AI for Cloud-Edge Computing/MANOLO$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101135782-MANOLO$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2021-125514NB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2024–158322OB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2024-159284NB-I00
000168539 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttps://creativecommons.org/licenses/by-nc/4.0/deed.es
000168539 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168539 700__ $$aPlou, Carlos$$uUniversidad de Zaragoza
000168539 700__ $$aMarcos, Miguel$$uUniversidad de Zaragoza
000168539 700__ $$0(orcid)0000-0003-3880-1842$$aUrcola, Pablo
000168539 700__ $$0(orcid)0000-0003-1183-349X$$aMontesano, Luis$$uUniversidad de Zaragoza
000168539 700__ $$0(orcid)0000-0002-5176-3767$$aMontijano, Eduardo$$uUniversidad de Zaragoza
000168539 700__ $$0(orcid)0000-0002-6741-844X$$aMartinez-Cantin, Ruben$$uUniversidad de Zaragoza
000168539 700__ $$0(orcid)0000-0002-7580-9037$$aMurillo, Ana C.$$uUniversidad de Zaragoza
000168539 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000168539 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000168539 773__ $$g264 (2026), 104619 [13 pp.]$$pComput. vis. image underst.$$tCOMPUTER VISION AND IMAGE UNDERSTANDING$$x1077-3142
000168539 8564_ $$s2798001$$uhttps://zaguan.unizar.es/record/168539/files/texto_completo.pdf$$yVersión publicada
000168539 8564_ $$s2512281$$uhttps://zaguan.unizar.es/record/168539/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168539 909CO $$ooai:zaguan.unizar.es:168539$$particulos$$pdriver
000168539 951__ $$a2026-02-09-14:43:01
000168539 980__ $$aARTICLE