Quantifying Full-Body Immersion

Humanity is at the forefront of yet another digital revolution, where the lines between real and virtual worlds are dissolving, reshaping how we perceive and interact with our surroundings. In this context, we introduce a transformative paradigm for immersive virtual experiences centered around whole-body kinetic interactions. Our approach redefines immersion through three distinct levels: audio-visual immersion, capturing sensory realism; physical immersion, delivering haptic feedback; and full-body immersion (FBI), where dynamic bodily interaction integrates seamlessly with virtual environments. At the core of this innovation lies a scalable, distributable platform based on modular robotic surface units inspired by the adaptive designs of nature. These units enable the rendering of immersive environments at any scale, from intimate personal experiences to expansive multi-user settings, dynamically adapting to interactions in real-time. The modular system distributes force, shape, and motion feedback throughout entire spaces, replicating the physical characteristics of the environment and enabling new depth of engagement through FBI. By combining scalability, adaptability, and dynamic physical engagement, this framework bridges the gap between real and virtual worlds. It offers an unprecedented level of immersion where users can engage their entire bodies in symbiotic interactions with the virtual space. This work not only advances immersive technology but also redefines how humans and virtual environments coexist, setting a foundation for a new era of human-environment synthesis.

Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data

Cardiac Output (CO) is a key parameter in the diagnosis and management of cardiovascular diseases. However, its accurate measurement requires right-heart catheterization, an invasive and time-consuming procedure, motivating the development of reliable non-invasive alternatives using echocardiography. In this work, we propose a self-supervised learning (SSL) pretraining strategy based on SimCLR to improve CO prediction from apical four-chamber echocardiographic videos. The pretraining is performed using the same limited dataset available for the downstream task, demonstrating the potential of SSL even under data scarcity. Our results show that SSL mitigates overfitting and improves representation learning, achieving an average Pearson correlation of 0.41 on the test set and outperforming PanEcho, a model trained on over one million echocardiographic exams. Source code is available at https://github.com/EIDOSLAB/cardiac-output.

Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation

Severe aortic stenosis is a common and life-threatening condition in elderly patients, often treated with Transcatheter Aortic Valve Implantation (TAVI). Despite procedural advances, paravalvular aortic regurgitation (PVR) remains one of the most frequent post-TAVI complications, with a proven impact on long-term prognosis. In this work, we investigate the potential of deep learning to predict the occurrence of PVR from preoperative cardiac CT. To this end, a dataset of preoperative TAVI patients was collected, and 3D convolutional neural networks were trained on isotropic CT volumes. The results achieved suggest that volumetric deep learning can capture subtle anatomical features from pre-TAVI imaging, opening new perspectives for personalized risk assessment and procedural optimization. Source code is available at https://github.com/EIDOSLAB/tavi.