Jesús Ortiz

Nicholas Cartocci, Antonios E. Gkikakis, Darwin G. Caldwell et al.

Developing a general-purpose wearable real-time fall-detection system is still a challenging task, especially for healthy and strong subjects, such as industrial workers that work in harsh environments. In this work, we present a hybrid approach for fall detection and prevention, which uses the dynamic model of an inverted pendulum to generate simulations of falling that are then fed to a deep learning framework. The output is a signal to activate a fall mitigation mechanism when the subject is at risk of harm. The advantage of this approach is that abstracted models can be used to efficiently generate training data for thousands of different subjects with different falling initial conditions, something that is practically impossible with real experiments. This approach is suitable for a specific type of fall, where the subjects fall without changing their initial configuration significantly, and it is the first step toward a general-purpose wearable device, with the aim of reducing fall-associated injuries in industrial environments, which can improve the safety of workers.

Javed Ahmad, Penggang Gao, Donatien Delehelle et al.

Neural scene representations such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have transformed how 3D environments are modeled, rendered, and interpreted. NeRF introduced view-consistent photorealism via volumetric rendering; 3DGS has rapidly emerged as an explicit, efficient alternative that supports high-quality rendering, faster optimization, and integration into hybrid pipelines for enhanced photorealism and task-driven scene understanding. This survey examines how 3DGS is being adopted across SLAM, telepresence and teleoperation, robotic manipulation, and 3D content generation. Despite their differences, these domains share common goals: photorealistic rendering, meaningful 3D structure, and accurate downstream tasks. We organize the review around unified research questions that explain why 3DGS is increasingly displacing NeRF-based approaches: What technical advantages drive its adoption? How does it adapt to different input modalities and domain-specific constraints? What limitations remain? By systematically comparing domain-specific pipelines, we show that 3DGS balances photorealism, geometric fidelity, and computational efficiency. The survey offers a roadmap for leveraging neural rendering not only for image synthesis but also for perception, interaction, and content creation across real and virtual environments.