The rapid development of Internet of Things (IoT) technology has significantly impacted various market sectors. According to Li et al (2024), an estimated 75 billion devices will be on the market in 2025. The healthcare industry is a target to improve patient care and ease healthcare provider burdens. Chronic respiratory disease is likely to benefit from their inclusion, with 545 million people worldwide recorded to suffer in patients using these devices can track their dosage, while healthcare providers can improve medication administration and monitor respiratory health (Soriano et al, 2020). The growing prevalence of IoT devices, including intelligent inhalers like the Propeller Health System Smart Inhaler, Breather Fit, and Lookee O2 Ring, underscores the increasing importance of network connectivity and software development. While IoT medical devices offer numerous benefits, they also come with security vulnerabilities that can expose patient data to cyber-attacks. It’s crucial to prioritize security measures in developing and deploying IoT medical devices, especially in personalized health monitoring systems for individuals with respiratory conditions. Addressing the security gaps and vulnerabilities in IoT devices is essential to ensure patient data’s safety and privacy. Efforts are underway to assess the security risks associated with intelligent inhalers and respiratory medical devices by understanding usability behaviour and technological elements to identify and address vulnerabilities effectively. This work analyses usability behaviour and technical vulnerabilities, emphasizing the confidentiality of information gained from Smart Inhalers. It then extrapolates to interrogate potential vulnerabilities with Implantable Medical Devices (IMDs). Our work explores the tensions in device development through the intersection of IoT technology and respiratory health, particularly in the context of intelligent inhalers and other breathing medical devices, calling for integrating robust security measures into the development and deployment of IoT devices to safeguard patient data and ensure the secure functioning of these critical healthcare technologies.

The cyber-physical immune system (CPIS) is an important field in healthcare security, a chemistry reactor-based, AI-friendly framework that shields healthcare and urban structures like the human immune system. This self-sufficient network identifies a threat, contains it, and neutralizes it in minutes without any disruption to company operations. John Arquilla (2021) coined this era of social networks and” netwars.” Cyberwarfare is on the rise around the world, and individuals, commercial companies, and institutions of all kinds are among the first to feel its sting. AI and real-time CPIS analytics allow for the expansion of coverage based on new threats, thus connecting cybersecurity and critical infrastructure protection. These ideas make it possible to identify the conceptual background, main components, and transformative characteristics of CPIS, as well as to consider its potential use in V2X communications, worldwide defense and medicine to overcome difficulties and facilitate further development. In this paper, we present the current threats in research with the prevailing challenges of current self-healing in CPIS for both global defense and healthcare systems. We explain the key components of the self-healing functionality of the CPIS in healthcare.

This paper compares the performance of BiLSTM and CNN+GRU deep learning models for Human Activity Recognition (HAR) on two WiFi-based Channel State Information (CSI) datasets: UT-HAR and NTU-Fi HAR. The findings indicate that the CNN+GRU model has a higher accuracy on the UT-HAR dataset (95.20%) thanks to its ability to extract spatial features. In contrast, the BiLSTM model performs better on the high-resolution NTU-Fi HAR dataset (92.05%) by extracting long-term temporal dependencies more effectively. The findings strongly emphasize the critical role of dataset characteristics and preprocessing techniques in model performance improvement. We also show the real-world applicability of such models in applications like healthcare and intelligent home systems, highlighting their potential for unobtrusive activity recognition.