EventSleep2: Sleep activity recognition on complete night sleep recordings with an event camera
Gallego, Nerea (Universidad de Zaragoza) ; Plou, Carlos (Universidad de Zaragoza) ; Marcos, Miguel (Universidad de Zaragoza) ; Urcola, Pablo ; Montesano, Luis (Universidad de Zaragoza) ; Montijano, Eduardo (Universidad de Zaragoza) ; Martinez-Cantin, Ruben (Universidad de Zaragoza) ; Murillo, Ana C. (Universidad de Zaragoza)
Resumen: 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.
Idioma: Inglés
DOI: 10.1016/j.cviu.2025.104619
Año: 2026
Publicado en: COMPUTER VISION AND IMAGE UNDERSTANDING 264 (2026), 104619 [13 pp.]
ISSN: 1077-3142
Financiación: info:eu-repo/grantAgreement/ES/DGA/T45-23R
Financiación: info:eu-repo/grantAgreement/EC/H2020/ 101135782/EU/Trustworthy Efficient AI for Cloud-Edge Computing/MANOLO
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2021-125514NB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2024–158322OB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2024-159284NB-I00
Tipo y forma: Article (Published version)
Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
Exportado de SIDERAL (2026-02-09-14:43:01)
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Idioma: Inglés
DOI: 10.1016/j.cviu.2025.104619
Año: 2026
Publicado en: COMPUTER VISION AND IMAGE UNDERSTANDING 264 (2026), 104619 [13 pp.]
ISSN: 1077-3142
Financiación: info:eu-repo/grantAgreement/ES/DGA/T45-23R
Financiación: info:eu-repo/grantAgreement/EC/H2020/ 101135782/EU/Trustworthy Efficient AI for Cloud-Edge Computing/MANOLO
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2021-125514NB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2024–158322OB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2024-159284NB-I00
Tipo y forma: Article (Published version)
Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. Exportado de SIDERAL (2026-02-09-14:43:01)
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Record created 2026-02-09, last modified 2026-02-09