Abstract
This study explores a novel approach for nonintrusive stress assessment based on Heart Rate Variability (HRV) features extracted from Electrocardiogram (ECG) signals acquired through a toilet seat-embedded sensor system. Building upon previous work by our group, we developed a framework for real-time Stress Index (SI) computation and evaluated its physiological relevance by analyzing the correlation with classical HRV parameters (SDNN, RMSSD, and LF/HF ratio) across subjects with and without cardiovascular disease. Results showed moderate negative correlations between SI and both SDNN and RMSSD, and a positive correlation with LF/HF ratio, supporting the validity of SI as a marker of autonomic stress. The system successfully differentiated autonomic stress responses between groups, with pathological participants exhibiting higher SI values, likely influenced by underlying cardiac conditions and the inherently stressful hospital environment. In contrast, individuals in contexts without cardiovascular disease exhibited lower SI values, consistent with parasympathetic predominance. These findings highlight the potential of ambient physiological monitoring using everyday objects for continuous and passive health assessment. The system's unobtrusive nature and capability to detect subclinical autonomic dysregulation support its application in personalised digital health, stress prevention, and early detection of autonomic dysfunction. © 2025 IEEE.

Abstract
Telecommunications and computer vision solutions have evolved significantly in recent years, allowing a huge advance in the functionalities and applications offered. However, these two fields have been making their way as separate areas, not exploring the potential benefits of merging the innovations brought from each of them. In challenging environments, for example, combining radio sensing and computer vision can strongly contribute to solving problems such as those introduced by obstructions or limited lighting. Machine learning algorithms, able to fuse heterogeneous and multi-modal data, are also a key element for understanding and inferring additional knowledge from raw and low-level data, able to create a new abstracting level that can significantly enhance many applications. This paper introduces the CONVERGE vision-radio concept, a new paradigm that explores the benefits of integrating two fields of knowledge towards the vision of View-to-Communicate, Communicate-to-View. The main concepts behind this vision, including supporting use cases and the proposed architecture, are presented. CONVERGE introduces a set of tools integrating wireless communications and computer vision to create a novel experimental infrastructure that will provide open datasets to the scientific community of both experimental and simulated data, enabling new research addressing various 6 G verticals, including telecommunications, automotive, manufacturing, media, and health.

Abstract
This chapter examines how Artificial Intelligence (AI) and Learning Analytics (LA) are transformingdistanceeducation, accelerated by the COVID-19 shift toe-learning. By using data from Learning Management Systems (LMS), these technologies can personalize learning, improve student retention, and automate tasks. AI, particularly machine learning, enables dynamic adaptation to student needs, while LA provides valuable insights for informed instructional decisions. However, ethical concerns, including data privacy and algorithmic bias, must be addressed to ensure equitable access and fair learning outcomes. The future of distance learning lies in responsible integration of AI and LA, creating immersive and inclusive educational experiences. © 2025 by IGI Global Scientific Publishing. All rights reserved.

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Abstract
Continuous, non-invasive Blood Pressure (BP) monitoring remains a key challenge in preventive cardiovascular healthcare. In this case study, we explore the potential of cuffless BP estimation using Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals acquired from the thighs of just one subject via a smart toilet seat equipped with built-in sensors. This unobtrusive setup enables passive data collection during routine bathroom use. We extracted timedomain and morphology-based features from the ECG and PPG signals, and trained three artificial intelligence regression models - Support Vector Regression (SVR), Random Forest (RF), and XGBoost - to estimate Systolic (SBP) and Diastolic (DBP) BP. The SVR model achieved the best performance, with Mean Absolute Error (MAE) values of 0. 2 5 and 0. 1 9, and Root Mean Square Error (RMSE) values of 0.41 and 0.35, for SBP (m m H g) and D B P( m H g), respectively. The Pearson Correlation Coefficient (PCC) exceeded 0.99 for both measures, indicating strong agreement between estimated and reference values. These findings support the integration of passive BP monitoring systems into everyday environments, promoting accessible and scalable solutions for long-term cardiovascular risk assessment. © 2025 IEEE.

Abstract