The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of attacks are possible. To understand the risks in this new landscape, it is important to understand the architecture of IoTHDs, operations, and the social dynamics that may govern their interactions. This paper aims to document and create a map regarding IoTHDs, lay the groundwork for better understanding security risks in emerging IoTHD modalities through a multi-layer approach, and suggest means for improved governance and interaction. We also discuss technological innovations expected to set the stage for novel exploits leading into the middle and latter parts of the 21st century.

Freezing of gait is a serious symptom of Parkinson’s disease that increases the risk of injury through falling, and reduces quality of life. Current clinical freezing of gait treatments fail to adequately address the fall risk posed by freezing of gait symptoms, and current real-time treatment systems have high false positive rates. To address this problem, we designed a closed-loop, non-intrusive, and real-time freezing of gait detection and treatment system, FoG-Finder, that automatically detects and treats freezing of gait. To evaluate FoG-Finder, we first collected 716 freezing of gait events from 11 patients. We then compared FoG-Finder against other real-time systems with our dataset. Our system was able to achieve a 13.4% higher F1 score and a 10.7% higher overall accuracy while achieving a reduction of 85.8% in the false positive treatment rate compared with other validated real-time freezing of gait detection and treatment systems. Additionally, FoG-Finder achieved an average treatment latency of 427ms and 615ms for subject-dependent and leave-one-subject-out settings, respectively, making it a viable system to treat freezing of gait in the real-world.

Tremor is a common symptom among Parkinson's disease (PD) patients at all stages. To measure tremor, we utilized IMU sensing data from the wrists while PD patients were drawing. With 30 patients' IMU sensing data obtained from standard tremor rating scale activities, we conducted data analysis for identifying any tremor episodes and extracting tremor amplitude. In this demo, we demonstrate that our preliminary analysis and results show the potential of measuring kinetic tremors effectively using these methods.

The proliferation of Internet of Things (IoT) devices has amplified botnet risks, while traditional network-based intrusion detection systems (IDSs) struggle under encrypted and/or sparse traffic. Power consumption offers an effective side channel for device-level detection. Yet, prior studies typically focus on a single model family (often a convolutional neural network (CNN)) and rarely assess generalization across devices or compare broader model classes. In this paper, we conduct unified benchmarking and comparison of classical (SVM and RF), deep (CNN, LSTM, and 1D Transformer), and hybrid (CNN + LSTM, CNN + Transformer, and CNN + RF) models on the CHASE’19 dataset and a newly curated three-class botnet dataset, using consistent preprocessing and evaluation across single- and cross-device settings, reporting both accuracy and efficiency (latency and throughput). Experimental results demonstrate that Random Forest achieves the highest single-device accuracy (99.43% on the Voice Assistant with Seed 42), while CNN + Transformer shows a strong accuracy–efficiency trade-off in cross-device scenarios (94.02% accuracy on the combined dataset at ∼60,000 samples/s when using the best-performing Seed 42). These results offer practical guidance for selecting models under accuracy, latency, and throughput constraints and establish a reproducible baseline for power-side-channel IDSs.

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.