Event Sleep 2 - Sleep activity recognition on complete night sleep recordings with an event camera

January 02, 2026

Nerea Gallego*, Carlos Plou*, Miguel Marcos, Pablo Urcola, Luis Montesano, Eduardo Montijano, Ruben Martinez-Cantin, Ana C Murillo.

Computer Vision and Image Understanding.

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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. Code and data are publicly available on the webpage: https://sites.google.com/unizar.es/eventsleep

Random Rotational Embedding Bayesian Optimization for Human-in-the-loop personalized music generation

November 21, 2025

Miguel Marcos, Lorenzo Mur-Labadia and Ruben Martinez-Cantin.

PLOS One.

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Generative deep learning models, such as those used for music generation, can produce a wide variety of results based on perturbations of random points in their latent space. User preferences can be incorporated in the generative process by replacing this random sampling with a personalized query. Bayesian optimization, a sample-efficient nonlinear optimization method, is the gold standard for human-in-the-loop optimization problems, such as finding this query. In this paper, we present random rotational embedding Bayesian optimization (ROMBO). This novel method can efficiently sample and optimize high-dimensional spaces with rotational symmetries, like the Gaussian latent spaces found in generative models. ROMBO works by embedding a low-dimensional Gaussian search space into a high-dimensional one through random rotations. Our method outperforms several baselines, including other high-dimensional Bayesian optimization variants. We evaluate our algorithm through a music generation task. Our evaluation includes both simulated experiments and real user feedback. Our results show that ROMBO can perform efficient personalization of a generative deep learning model. The main contributions of our paper are: we introduce a novel embedding strategy for Bayesian optimization in high-dimensional Gaussian sample spaces; achieve a consistently better performance throughout optimization with respect to baselines, with a final loss reduction of 16%-31% in simulation; and complement our simulated evaluations with a study with human volunteers (n = 16). Users working with our music generation pipeline find new favorite pieces 40% more often, 16% faster, and spend 18% less time on pieces they dislike than when randomly querying the model. These results, along with a final survey, demonstrate great performance and satisfaction, even among users with particular tastes.