The study, published in Frontiers in the Internet of Things, examined the Beurer BM 95 upper arm blood pressure monitor with ECG function, the AliveCor KardiaMobile 6L six-lead ECG monitor, and the OMRON Complete blood pressure and ECG monitor. All three devices pair with companion Android apps over Bluetooth Low Energy, the power-sipping variant of Bluetooth that has become the default radio for wearable and portable health technology. Led by Alex Akinbi and Preethi Paul Raj from the university’s Department of Computing and Mathematics, the team built its investigation around a deliberately proactive philosophy: rather than waiting for a breach and reconstructing what happened, they modeled the threats first, then attacked the devices in a controlled laboratory setting to see which of those threats were real.

That modeling work began with a systematic literature review following established software engineering guidelines. The researchers searched five major digital libraries, including IEEE Xplore, ScienceDirect, the ACM Digital Library, Google Scholar and SpringerLink, for peer-reviewed studies published between January 2020 and January 2025 that addressed security threats and threat modeling for medical Internet of Things systems. The initial search returned 574 publications, which the team whittled down through successive rounds of inclusion and exclusion filtering to 34 relevant papers. From those, they extracted a catalogue of 48 distinct threats spanning device sensors, network communication, cloud storage and companion smartphones, which they consolidated into 36 threats specific to portable ECG ecosystems, organized into five attack categories: companion device attacks, wireless network attacks, cloud and server-side attacks, software and firmware exploitation, and side-channel attacks.

To turn that catalogue into an actionable test plan, the researchers combined two complementary threat modeling techniques. STRIDE, a framework originally developed at Microsoft, was used to understand the security boundaries of each component in the ECG ecosystem, from the sensor electrodes to the smartphone app to the cloud backend. Attack trees, a technique dating back to security pioneer Bruce Schneier, were then used to map out the concrete paths an attacker could take toward a specific goal, with each branch of the tree representing a sub-goal or attack method. Some branches required multiple weaknesses to be chained together before a breach occurred; others could cause a breach on their own. This dual approach, the authors argue, addresses a gap in prior work, which often tested medical devices without a reproducible, systematic methodology that other security analysts could follow.

The experimental setup was deliberately grounded in realistic adversarial capabilities. A Google Pixel 8a running Android 14 served as the companion smartphone, paired with each ECG device in turn. The phone was rooted using Magisk, a widely available rooting tool, with the Shamiko module deployed to hide the rooted status from apps that attempt to detect and block compromised devices. BLE traffic was captured with an nRF52840 dongle acting as a hardware sniffer, alongside Wireshark for protocol analysis and the Android Bluetooth HCI snoop log retrieved through the Android Debug Bridge. Forensic imaging and app data analysis were performed with Magnet Axiom Process and Autopsy. The study received ethical approval from Manchester Metropolitan University’s Research Ethics Committee, and all ECG recordings collected from volunteers were anonymized before analysis.

The most striking finding concerns the Beurer BM 95. Traffic analysis revealed that the device uses the BLE ‘Just Works’ association model at Security Mode 1, Level 1, which provides no encryption and no authentication whatsoever. In packet captures, the team could read blood pressure measurements, heart rate values, status information and even a user identifier flowing between the device and the smartphone in plaintext, visible to anyone within radio range with an inexpensive sniffer. Because no secure pairing is implemented, the researchers note that more sophisticated man-in-the-middle attacks and secure simple pairing attacks become possible in specific scenarios, including the injection of malicious readings into the data stream. The device runs Bluetooth 4.0, and the analysis linked it to two known vulnerabilities, CVE-2023-24023 and CVE-2019-9506, carrying medium and high severity scores respectively.

The KardiaMobile 6L and OMRON Complete fared somewhat better, but not well enough to be reassuring. Both devices implement Security Mode 1, Level 2, which pairs through Just Works but at least encrypts the connection using a Long Term Key negotiated during pairing. Packet captures showed the LE Start Encryption command sequence, with the LTK, encrypted diversifier and random number exchanged to generate the session key. However, Just Works pairing offers no protection against man-in-the-middle attacks, because there is no mechanism for the user to verify that they are pairing with the legitimate device rather than an impostor. Both devices also run legacy Bluetooth versions, 4.2 and 4.1 respectively, which expose them to the KNOB attack, a downgrade technique that manipulates the encryption key negotiation to force keys with as little as one byte of entropy, rendering them trivially brute-forceable. Offline brute-force attacks on legacy pairing and BlueMirror attacks, which mirror legitimate device responses to trick systems into accepting unauthorized commands, round out the threat picture.

The team also probed the firmware itself. On both the Beurer BM 95 and the OMRON Complete, they identified the memory chip as an SST26VF032B, a 32-megabit serial flash memory, and used a CH341A programmer to extract the stored firmware. The recovered data turned out to be encrypted with OpenPGP, and without the encryption key the researchers could not decrypt it, a defensive measure that at least raises the bar for firmware tampering. The smartphone app analysis proved more revealing. On a rooted device, the Kardia app refused to run until the Shamiko module concealed the root status, and its SQLite database stored ECG results, timestamps, heart rates in beats per minute, device serial numbers and paired device records, alongside proprietary .atc files that can be parsed to visualize full six-lead ECG waveforms. The Omron Connect and Beurer HealthManager Pro apps ran on the rooted phone without any restriction, storing ECG waveform images and Realm database files named with hex-encoded user identifiers, all recoverable by an attacker who gains root access through malware.

The researchers were careful to acknowledge the limits of their work. Side-channel attacks, which leak cryptographic keys through power consumption or electromagnetic emanations, and cloud and server-side attacks were excluded from testing due to tooling limitations and ethical concerns about handling sensitive data on third-party infrastructure. That omission, they write, constrains the generalizability of the findings, particularly since real-world deployments increasingly connect such devices to hospital networks and analytics services. Still, the comparative verdict was unambiguous: while all three devices showed weaknesses, the Beurer BM 95 presents a significantly higher risk than the KardiaMobile 6L or the OMRON Complete, primarily because it transmits user data without any encryption and carries two known CVEs where the others carry one each.

The countermeasures the study proposes are technically straightforward but practically difficult. The gold standard is BLE Security Mode 1, Level 4, which uses LE Secure Connections with Elliptic Curve Diffie-Hellman key exchange and pairing models such as Numeric Comparison, Passkey Entry or out-of-band pairing, all of which resist man-in-the-middle and eavesdropping attacks. Replay attacks can be defeated with nonces and timestamps, and KNOB attacks by enforcing minimum key lengths during negotiation. The catch is that Bluetooth version support is baked into the radio adapter on the device’s motherboard, so firmware updates cannot upgrade a Bluetooth 4.0 chip to modern security. On the app side, the authors recommend multi-layered root detection and encrypting all health data with robust algorithms such as AES, with keys secured in the Android Keystore. As connected healthcare continues to expand, the study’s central message is that security must be designed into these ecosystems from the start, because the data at stake, the intimate electrical signature of a human heartbeat, is exactly the kind of information patients cannot afford to broadcast in the clear.

Subject of Research: Cybersecurity vulnerabilities in portable ECG medical IoT devices and their Bluetooth ecosystems

Article Title: A systematic security analysis of medical internet of things (MIoT) ecosystems in threat modeling scenarios

Article References: A systematic security analysis of medical internet of things (MIoT) ecosystems in threat modeling scenarios. (n.d.). https://doi.org/10.3389/friot.2025.1712430

Image Credits: AI Generated

DOI: 10.3389/friot.2025.1712430

Keywords: medical IoT, ECG monitors, Bluetooth Low Energy, threat modeling, penetration testing, cybersecurity, vulnerability analysis, STRIDE, attack trees, patient data privacy, firmware extraction, Android apps

The appeal of contactless monitoring is easy to understand. Traditional hospital instrumentation, from finger-clip pulse oximeters to adhesive electrode patches and inflatable blood pressure cuffs, works well enough in controlled clinical settings, but it is uncomfortable for long-term wear, can irritate fragile skin in elderly and neonatal patients, and requires constant replacement and maintenance. Non-contact systems sidestep all of this by capturing physiological information at a distance. Cameras read the subtle color shifts in facial skin caused by pulsing blood flow. Radar transmitters bounce millimeter-wave signals off the chest and detect the millimeter-scale movements of breathing and heartbeat. Wi-Fi routers measure how the human body perturbs ambient wireless channels. Acoustic sensors and infrared detectors add further modalities, and modern machine learning pipelines fuse these raw signals into usable clinical estimates.

What distinguishes the new review is its system-level perspective. Previous surveys tended to examine a single sensing technology, such as radar or computer vision, or focused narrowly on signal processing algorithms. The authors instead characterized every included study across five dimensions: sensing modality, system architecture, the physiological parameter being targeted, the validation setting, and reported performance. They also evaluated methodological quality and, critically, asked how often privacy-preserving mechanisms had actually been built into the systems, an unusually honest question for a field whose primary instruments are cameras and radio transmitters pointed at people in intimate moments of illness.

The results reveal a field of striking imbalance. Radar and vision-based systems dominate the literature, together accounting for roughly three-quarters of all studies analyzed. These two technologies have matured fastest because their underlying physics and computer vision toolchains are comparatively well developed, and because both can plausibly reach the accuracy thresholds needed for clinical triage. By contrast, approaches based on sound, Wi-Fi sensing, embedded sensors integrated into furniture, and mixed-modality combinations remain at an earlier stage of validation, with fewer studies, smaller cohorts, and less standardized evaluation.

Even more striking is the concentration on two vital signs. Respiration rate appears in 83 percent of the reviewed studies, and heart rate in 67 percent, a significant overlap given that many systems extract both from the same chest-motion or skin-color signal. No other physiological parameter exceeds 11 percent of the corpus. Blood pressure, oxygen saturation, and body temperature, all staples of routine clinical assessment, remain largely unaddressed by contactless methods. The reason is physics as much as preference: breathing and heartbeat produce measurable mechanical displacement and periodic optical change that sensors can resolve with modest hardware, whereas blood pressure and SpO₂ estimation from a distance requires subtle modeling of vascular properties that current remote techniques capture only indirectly and with limited reliability.

Perhaps the most sobering finding concerns measurement itself. The review found that heterogeneous reporting metrics across studies prevent direct statistical comparison of performance between modalities. One radar study might report mean absolute error on healthy volunteers at one meter; a vision study might report correlation coefficients on hospitalized patients at three meters under different lighting. Without standardized datasets, common evaluation protocols, and agreed-upon error metrics, clinicians and hospital administrators have no rigorous way to compare a radar system against a camera-based alternative, and the field’s headline accuracy numbers remain effectively incommensurable. This absence of benchmarking, the authors argue, is now a primary brake on clinical translation.

When it comes to deployment, the review’s central message is that context should drive technology selection, not laboratory accuracy rankings. Radar excels in hospital wards and intensive care units, where patients may be covered by blankets, moving restlessly, or positioned unpredictably, conditions that defeat optical systems but not penetrating radio waves that measure motion through fabric and darkness. Vision and acoustic systems, meanwhile, suit structured clinical spaces with stable lighting and predictable geometry, such as examination rooms and sleep laboratories, where a camera’s rich spatiotemporal information can be fully exploited. Embedded sensor designs, built into eldercare beds and chairs, offer a middle path: technically not remote, but requiring no wearable and no user cooperation, making them practical for dementia care and nursing homes where continuous compliance cannot be assumed.

For settings where the stakes are highest, the review points to mixed radar and vision systems as the emerging precision tool. By fusing the robustness of radar with the richness of optical sensing, hybrid architectures can provide redundant, mutually validating measurements suitable for neonatal monitoring and postoperative care, where a single missed apnea event or unrecognized desaturation can be catastrophic. The trade-offs are real: greater hardware complexity, harder signal fusion, and higher cost. But as both sensing strands mature, the fusion approach increasingly looks like the endpoint toward which precision-critical contactless monitoring is converging.

The review does not shy away from the field’s persistent failures. Signal reliability in uncontrolled environments remains the fundamental technical challenge: ambient light changes, multipath radio reflections, other people moving through the scene, pets, fans, and vibrating beds all contaminate the weak physiological signals these systems hunt for. Privacy and ethical concerns compound the problem, since cameras and radar implicitly surveil patients, families, and sometimes entire rooms, raising questions about consent, data ownership, and secondary use that few current systems adequately address. Finally, poor integration with existing healthcare infrastructure, electronic health records, nursing alarm systems, and clinical workflows, means that even accurate prototypes often die at the pilot stage, unable to slot into the operational reality of hospitals.

As a forward-looking contribution, the authors propose a conceptual federated, edge-based learning framework, explicitly presented as a design proposal rather than a validated system. The core idea is compelling: instead of shipping raw camera and radar data to central servers, processing would occur at the edge device itself, with only model updates, not identifiable physiological data, shared across institutions through federated learning. Such an architecture would simultaneously address privacy, reduce bandwidth, and allow hospitals to pool learning without pooling patient data. Combined with the review’s other prescriptions, standardized open datasets, unified evaluation metrics, and co-design with clinical end users, this roadmap suggests that contactless vital-sign monitoring may finally be approaching the inflection point where it leaves the demonstration stage and becomes an invisible, ambient layer of everyday clinical care.

Subject of Research: Non-contact medical IoT systems for monitoring physiological biomarkers

Article Title: A systematic review of non-contact medical IoT systems for physiological biomarker monitoring

Article References: Banik, B., Ghose, S., Nahiun, A. H., Lin, Y., Shalaginov, A., & Ghose, D. (2026). A systematic review of non-contact medical IoT systems for physiological biomarker monitoring. Frontiers in Medical Technology, 8, Article 1917332. https://doi.org/10.3389/fmedt.2026.1917332

Image Credits: AI Generated

DOI: 10.3389/fmedt.2026.1917332

Keywords: non-contact monitoring, medical IoT, radar sensing, vision-based sensing, respiration rate, heart rate, vital signs, federated learning, edge computing, patient monitoring, healthcare technology, PRISMA review

Federated learning is not a new idea. Instead of shipping data to a central server, the technique sends the model to the data: each hospital trains the algorithm locally on its own patients and shares only the resulting parameter updates, which are then averaged into a global model. The problem, as the researchers emphasize, is that sharing gradients is not the same as sharing nothing. Sophisticated adversaries can reconstruct individual patient records from model updates, a class of threats known as gradient leakage and membership inference attacks. On top of that, real hospital networks are messy places, with devices of wildly different computing power, unreliable wireless links, and data distributions that differ from one ward to the next, a condition researchers call non-IID data. Conventional federated frameworks tend to stumble under exactly these conditions.

FedDeepRiskNet++ attacks the problem with four interlocking modules, each addressing a different failure mode. The first is differential privacy, a mathematically rigorous method of protecting individuals in a dataset by injecting carefully calibrated noise into the local model updates before they leave the hospital. The noise is large enough to make it impossible to infer whether any single patient’s record contributed to a result, but small enough that the aggregate statistical signal survives. The second module is secure aggregation based on CKKS homomorphic encryption, a scheme introduced by cryptographers Cheon, Kim, Kim and Song that allows arithmetic to be performed directly on encrypted numbers. In practice, this means the central server can combine the encrypted updates from all participating hospitals and never see any of them in decrypted form.

The third and arguably most novel component is what the authors call adaptive privacy-budget scheduling, or APBS. Differential privacy is governed by a parameter called epsilon, the privacy budget: a smaller epsilon means stronger privacy but noisier, less useful updates, while a larger epsilon improves accuracy at the cost of confidentiality. Most systems fix this value once and live with the trade-off. APBS instead treats it as a dynamic dial, adjusting the privacy budget in real time according to each client’s reliability, energy status, and communication conditions. A well-connected, trustworthy hospital node running on stable power can operate with tighter privacy; a struggling device on a congested network can be allocated a different budget so that the global model’s utility does not collapse. The researchers report that this adaptive scheduling significantly improves the privacy-utility trade-off compared with static allocation, a conclusion they back up with ablation experiments that remove individual modules and measure the damage.

The fourth module tackles a less glamorous but equally critical bottleneck: communication. In large-scale federated systems, the sheer volume of model updates shuttling between hospitals and the aggregation server can overwhelm networks and drain the batteries of edge devices. FedDeepRiskNet++ employs adaptive gradient sparsification, transmitting only the most significant portions of each update rather than the full parameter set, combined with what the authors describe as encryption-aware communication packaging, which bundles the sparse updates so that the overhead of homomorphic encryption does not erase the savings. The result, according to the paper, is a reduction in communication cost of approximately 23 percent compared with standard approaches, with cumulative communication overhead held to 165 megabytes.

The performance numbers are the study’s headline claim. Tested on two widely used clinical datasets, PhysioNet 2023 and MIMIC-III, the framework achieved 91.2 percent classification accuracy and a 90.1 percent F1-score, a balanced measure of precision and recall that matters greatly in medical settings where both missed diagnoses and false alarms carry costs. Crucially, these results were obtained while maintaining a low privacy budget of epsilon equal to 2.9, a level that represents meaningful formal privacy protection rather than a token gesture. The authors compared their system against a series of established baselines, including the canonical FedAvg algorithm, the differentially private DP-FedAvg, the privacy-aware hierarchical framework PAHFL, Health-FedNet, and their own earlier FedDeepRiskNet, and found that the new framework also converged faster under heterogeneous non-IID hospital conditions, meaning it needed fewer communication rounds to reach a usable model.

Why does this matter beyond the benchmark tables? The Internet of Medical Things has expanded explosively, with connected monitors, infusion pumps, wearable sensors and imaging devices generating continuous streams of clinical data. Predictive models trained on a single hospital’s population inevitably inherit its blind spots, and the COVID-19 pandemic demonstrated both the promise and the difficulty of cross-institutional collaboration, as researchers who built federated models for predicting clinical outcomes in COVID-19 patients learned firsthand. A framework that combines formal privacy guarantees, cryptographic protection, and communication efficiency could make it practical for networks of hospitals, including smaller facilities with limited infrastructure, to contribute to large-scale diagnostic models without exposing their patients or exhausting their networks.

The study also reflects a broader shift in how the machine learning community thinks about privacy. For years, differential privacy and homomorphic encryption were seen as alternatives, each too costly to combine with the other. FedDeepRiskNet++ is part of a growing body of work arguing that layered defenses, noise plus encryption plus sparsification, can coexist if each layer is engineered with the others in mind. The adaptive scheduling of the privacy budget is particularly significant because it acknowledges a truth that static designs ignore: privacy is not a fixed setting but a resource that must be spent wisely across a fleet of heterogeneous devices. The work builds on the team’s earlier energy-aware FedDeepRiskNet system for multi-hospital environments, extending it with the encryption and scheduling machinery needed for security-critical deployments.

There are, of course, limits to what any single study can establish. The experiments were conducted on retrospective datasets rather than in live hospital deployments, and the authors note that no datasets were newly generated or analyzed beyond the benchmark evaluations. Real-world rollouts will need to confront regulatory audits, adversarial participants, and the operational realities of hospital IT departments. The research was funded by the Deanship of Graduate Studies and Scientific Research at Jazan University, and the team includes Noha Mostafa of Jazan’s computer sciences department and Ayman A. Alharbi of Umm Al-Qura University. Still, the central demonstration stands: strong privacy protection, communication efficiency, and high diagnostic reliability can coexist within large-scale federated healthcare systems. If frameworks like FedDeepRiskNet++ mature from benchmarks into clinical infrastructure, the era in which hospitals must choose between protecting their patients and advancing medicine may finally be drawing to a close.

Subject of Research: Privacy-preserving federated learning for secure medical Internet of Things systems

Article Title: FedDeepRiskNet++: a privacy-enhanced federated learning framework for secure and scalable medical IoT systems

Article References: FedDeepRiskNet++: a privacy-enhanced federated learning framework for secure and scalable medical IoT systems. (n.d.). https://doi.org/10.1007/s10586-026-06616-6

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06616-6

Keywords: federated learning, differential privacy, homomorphic encryption, medical IoT, healthcare AI, data privacy, secure aggregation, communication efficiency, non-IID data, clinical prediction, edge computing, privacy budget