Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases’ symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.

The paper presents the design and implementation of the Cybersecurity Social Network Analyser (CSNA) platform. The CSNA is a software system for real time automated analysis of cybersecurity-related information available on Social Networks, with a focus on Twitter. Its aim is to uncover cybersecurity breach related discussions and posts, identify emerging cybersecurity incidents and provide, in a timely fashion, early warning alerts. The CSNA platform presents the results from the analysis of the gathered information in a variety of graphical formats for easy visualisation of potential threats within user-specified time windows of interest. We complement our description with a demonstration of the real time information provided by the platform during its operation on the Twitter social network.

In the evolving domain of Human Activity Recognition (HAR) using Internet of Things (IoT) devices, there is an emerging interest in employing Deep Generative Models (DGMs) to address data scarcity, enhance data quality, and improve classification metrics scores. Among these types of models, Generative Adversarial Networks (GANs) have arisen as a powerful tool for generating synthetic data that mimic real-world scenarios with high fidelity. However, Human Gesture Recognition (HGR), a subset of HAR, particularly in healthcare applications, using time series data such as allergic gestures, remains highly unexplored. In this paper, we examine and evaluate the performance of two GANs in the generation of synthetic gesture motion data that compose a part of an open-source benchmark dataset. The data is related to the disease identification domain and healthcare, specifically to allergic rhinitis. We also focus on these AI models' performance in terms of fidelity, diversity, and privacy. Furthermore, we examine the scenario if the synthetic data can substitute real data, in training scenarios and how well models trained on synthetic data can be generalized for the allergic rhinitis gestures. In our work, these gestures are related to 6-axes accelerometer and gyroscope data, serving as multi-variate time series instances, and retrieved from smart wearable devices. To the best of our knowledge, this study is the first to explore the feasibility of synthesizing motion gestures for allergic rhinitis from wearable IoT device data using Generative Adversarial Networks (GANs) and testing their impact on the generalization of gesture recognition systems. It is worth noting that, even if our method has been applied to a specific category of gestures, it is designed to be generalized and can be deployed also to other motion data in the HGR domain.

Cough sounds are accessible, non-invasive biomarkers for respiratory disease assessment and can be captured using consumer-grade smartphones. Existing approaches typically focus solely on cough detection or rely on server-based deep learning for disease classification, which limits deployability and raises privacy concerns. Small, imbalanced cough datasets further hinder model generalization. To develop a multilayer, smartphone-compatible AI framework for automated cough detection and respiratory disease classification, and to propose a pioneering generative augmentation strategy utilizing a suite of five Variational Autoencoder (VAE) variants and a probabilistic cough-level fusion mechanism to improve disease classification under severe data scarcity and the limitations of conventional audio augmentation techniques. The proposed framework consists of three AI modules: (1) A Cough Detection Module (CDM) that performs real-time cough event detection and segmentation from continuous audio using lightweight models optimized for on-device execution. (2) A Disease Analysis Module (DAM) that classifies cough events into asthma, COVID-19, or healthy classes using parallel Support Vector Machine classifiers and a probabilistic cough-level fusion strategy. (3) A Generative Augmentation Module (GAM) employing five distinct VAE architectures. This module uniquely operates across the time-frequency domain for latent feature optimization while reconstructing samples in the time-domain to ensure acoustic verifiability. The CDM provides reliable segmentation across heterogeneous recording conditions. The DAM achieves strong discriminability between asthma, COVID-19, and healthy coughs using compact cepstral and spectral features. The multi-variant GAM framework demonstrates superior efficacy in alleviating class imbalance specifically, the cross-domain (time-frequency to time-domain) reconstruction allows for the clinical verification of synthetic biomarkers. All system components operate in real time on commodity Android hardware. This integrated framework addresses key limitations in dataset availability, model generalizability, and deployability, introducing an interpretable generative approach that demonstrates the feasibility of smartphone-based acoustic sensing as a scalable and privacy-preserving tool for respiratory health monitoring.

Air pollution has been recognised as the single biggest environmental threat to human health within the European Union (EU). Indoor air pollution has received less attention than outdoor air pollution, even though we spend most of our time indoors. It is associated with a variety of short‐ and long‐term health effects. These include allergies and asthma with a key role for the exposure to bioaerosols and synergistic interactions between chemical and biological pollutants. Given the dramatic rise of allergic diseases, the prevention of symptom exacerbations and the new onset of disease are of great importance, particularly for vulnerable populations such as children. The lack of a unified EU‐wide approach involving all stakeholders represents a major unmet need. The EU‐funded IDEAL Cluster comprises seven research projects to advance knowledge on indoor air quality (IAQ) and its impact on health, inform policies and thus promote healthier indoor environments. The ‘SynAir‐G’ project forms part of this cluster and was launched in 2022 with the participation of 18 partners from 11 countries. It focuses on schools to explore synergistic interactions between chemical and biological pollutants based on the hypothesis that these modulate major health effects. SynAir‐G includes the development of low‐cost sensors, collection of health outcomes, modelling of pollution dynamics and the development of mitigation solutions. The knowledge will be circulated to all stakeholders and the public aiming to establish standards and health‐based guidelines. The scope of this position statement is (1) to review the current evidence on indoor air pollution, its impact on health and particularly on children with allergies and asthma; (2) to give an overview of the European regulatory framework; (3) to describe knowledge gaps, challenges and unmet needs; and (4) to present SynAir‐G′s project goals and propose a roadmap to improve IAQ for children with asthma and allergies.