Search Results (126)

This article proposes, to the best of our knowledge, the first dual-port compact dual-polarized ultrawideband wearable textile antenna covering lower UHF bands for off-body communications in Internet-of-Things-based wireless body area network (IoT-based WBAN) scenarios. The antenna covers key bands for diverse services, including the 470–510 MHz LoRa WAN, 700 MHz offline emergency communication, 900 MHz NB-IoT, and 1–1.2 GHz satellite internet bands. The antenna adopts a square-ring loaded wide slot structure and a multi-mode resonant feeding structure to achieve ultrawideband operation. Moreover, it utilizes oppositely placed advanced microstrip feeding networks to excite the horizontal and vertical polarization modes, respectively, and four narrow slots around the wide slot to extend the current path, thus enabling a compact size of 0.30 × 0.28 × 0.0035 λ_l³ (where λ_l is the largest operating wavelength). Measured −10 dB impedance bandwidths are 119.1% (0.35–1.38 GHz) for Port 1 and 115.9% (0.39–1.37 GHz) for Port 2 on the human body, with more than 19 dB port isolation over the operating band. The measured average gains are about 4.21 dBi for Port 1 and 3.54 dBi for Port 2 on the human body, respectively. Specific absorption rate analysis confirms compliance with the IEEE C95.1 limit at 0.5 W input power. Wireless transmission experiments at IoT bands further validate reliable off-body links with excellent signal-to-noise ratios for both polarizations. The antenna shall be very attractive for off-body communications in IoT-based WBAN scenarios. Full article

Wireless sensor networks underpin the Internet of Medical Things (IoMT), where connected medical devices and wearable body-area sensors stream patient telemetry across hospital networks, and securing this traffic is safety-critical. Machine learning intrusion-detection systems for the IoMT are usually evaluated within a single public dataset, where they report near-perfect detection scores; whether that accuracy predicts performance in a different hospital has not been measured systematically. We frame the deployed detector as a lightweight human-in-the-loop edge AI agent that observes local traffic, asks an analyst to label a small set of representative flows, trains a tiny model on-premises, and monitors the traffic distribution for drift. Using cross-corpus shift across four public datasets, comprising three sensor-network corpora (WUSTL-EHMS-2020, CIC-IoMT-2024, TON-IoT) and an unrelated enterprise-network corpus, as a reproducible stand-in for cross-hospital deployment, we find the shift is near its theoretical maximum on all twelve transfer directions, that unlabeled domain-adaptation methods collapse on the hardest medical-telemetry targets, and that external pretraining adds no measurable benefit once training schedules are matched, consistent with a domain-adaptation error bound that the measured divergence renders vacuous. The same 1206-parameter network trained from scratch on ten to fifty locally labeled flows matches or exceeds every transfer alternative across all four corpora; on the six sensor-network transfer directions it also outperforms larger and classical local models. A closed-loop evaluation on simulated drifting streams shows the calibrated trigger detects drift within two 500-flow windows at under one false alarm per hundred stationary windows, retraining restores accuracy, and the agent tolerates ten percent analyst error for about five F1 points; we also map the detector’s exposure to white-box evasion and targeted label poisoning. Full article

Wireless Body Area Networks (WBANs) have become an essential component of modern Internet of Medical Things (IoMT) healthcare systems, enabling continuous monitoring of patient physiological signals through wearable sensors. Despite their advantages, WBAN environments remain highly prone to cyber threats, privacy breaches, and single points of failure. To address these risks, this work proposes a Hybrid Multi-Metric Anomaly Detection (HM-MAD) framework deployed on the NodeMCU-32S platform with BLE 5.0 connectivity for secure continuous glucose monitoring (CGM) data transmission. The detection model simultaneously analyses physiological signals, system-level parameters, and network-level communication metrics, enabling the reliable identification of multiple cyberattacks. The proposed system focuses on securing data transmission against relay attacks, where attackers induce communication delay without modifying payloads, potentially leading to false glucose readings, improper insulin dosage delivery, unauthorized control or denial-of-service. The Convolutional Neural Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM) model classifies attack types including timing manipulation, replay attacks, power glitches, firmware tampering, and sensor spoofing. Experimental evaluation demonstrates that the proposed CNN + BiLSTM framework achieves 94.6% detection accuracy with an average inference latency of 15 ms, representing a 50% latency reduction compared to Transformer-based intrusion detection models (30 ms), while simultaneously reducing computational overhead by 28% in terms of floating-point operations and memory utilization. These results indicate that the HM-MAD framework provides an effective and scalable solution for protecting resource-constrained IoMT healthcare systems against emerging cyber threats. Full article

Conductive silicone interfaces are promising polymeric materials for wearable bioelectronic systems because they combine electrical continuity with elastomeric compliance, environmental durability, and moldability. In low-voltage wearable microcurrent interfaces, however, functional performance is governed not only by intrinsic material conductivity, but also by conductive network continuity, molded geometry, interfacial contact, and transient electrical response. In this study, we developed a spike-structured conductive silicone interface using a commercially available electrically conductive two-component silicone rubber and investigated its structure–electrical property relationships as a volume-resistive polymer interface. The interface consisted of a conductive silicone body with protrusions 7 mm in height and 3.6 mm in diameter, supported by a 1 mm base layer and electrically integrated through an Ag-paste-connected upper conduction region. Using a representative electrode-level resistance of 47.08 Ω, the geometry-derived apparent interfacial resistive response was estimated as 18.0 Ω·cm for the three-spike configuration and 24.0 Ω·cm for the four-spike configuration. The corresponding effective conductive areas were 0.305 cm² and 0.407 cm², respectively, giving analytical current-density amplification factors of 9.82 and 7.37 relative to a planar 3 cm² reference interface. Positional resistance mapping yielded an overall mean resistance of 47.80 ± 4.57 Ω, indicating acceptable electrical reproducibility across the structured conductive silicone interface. In addition, oscilloscope-based transient response analysis under a 5 V, 1 kHz square-wave input showed that the conductive silicone interface maintained the overall pulse waveform while showing a modest reduction in overshoot from 3.4 ± 0.1% to 2.7 ± 0.1%, with FFT traces used as qualitative waveform-monitoring displays. Formulation-dependent comparison further showed that increasing the silicone-rich fraction increased the measured resistance from 105 Ω to 145 Ω, whereas increasing conductive carbon loading reduced resistance but aggravated surface transfer. These results show that the conductive silicone interface functions not simply as a soft conductor, but as a volume-resistive, geometry-defined current-transfer medium whose behavior is governed by the coupled effects of conductive network formation, spike architecture, electrode-level resistance, and transient pulse response. This study provides a practical materials/interface design framework for spike-structured conductive silicone electrodes in wearable bioelectronic and electroceutical devices. Full article

This paper investigates an artificial intelligence (AI)-enhanced visible light communication (VLC) system for body area networks (BANs) based on a hybrid modulation framework that jointly employs quadrature amplitude modulation (QAM) and pulse-position modulation (PPM). The dual-modulation strategy leverages the high spectral efficiency of QAM together with the robustness of PPM to light-emitting diode (LED) nonlinearity and timing distortions, enabling simultaneous high-rate and reliable communication, two essential requirements in BAN applications. To address the nonlinear response of light-emitting diodes and the variability in indoor optical channels, the system integrates classical predistortion techniques with a deep learning equalizer combining convolutional neural network (CNN)–transformer layers. This hybrid model captures both local and long-range distortion patterns, improving symbol reconstruction for both modulation branches. The study further examines pilot-assisted equalization and adaptive bit loading, showing that these strategies strengthen link robustness under diverse channel conditions while enhancing spectral efficiency. The proposed architecture demonstrates that combining dual modulation with AI-driven equalization and adaptive transmission strategies leads to a more resilient and efficient VLC system, well-suited for the dynamic constraints of wearable and body-centric communication environments. Full article

Accurate cardiac output (CO) measurement is vital for hemodynamic management; however, it usually requires invasive monitoring, which limits its continuous and out-of-hospital use. Wearable sensors integrated with deep learning offer a noninvasive alternative. This study developed and validated a lightweight deep learning model using wearable electrocardiography (ECG) and photoplethysmography (PPG) signals to predict CO and examined whether cardiac index-based normalization (Cardiac Index (CI) = CO/body surface area) improves performance. Twenty-seven patients who underwent cardiac surgery and had pulmonary artery catheters were prospectively enrolled. Single-lead ECG (HiCardi+ chest patch) and finger PPG (WristOx2 3150) were recorded simultaneously and processed through an ECG–PPG fusion network with cross-modal interaction. Three models were trained as follows: (1) CI prediction, (2) direct CO prediction, and (3) indirect CO prediction. The total number of CO = predicted CI × body surface area. Reference values were derived from thermodilution. The CI model achieved the best performance, and the indirect CO model showed significant reductions in error/agreement metrics (MAE/RMSE/bias; p < 0.0001), while correlation-based metrics are reported descriptively without implying statistical significance. The Pearson correlation coefficient (PCC) and percentage error (PE) for the indirect CO estimates (PCC = 0.904; PE = 23.75%). The indirect CO estimates met the predefined PE < 30% agreement benchmark for method-comparison; this is not a universal clinical standard. These results demonstrate that wearable ECG–PPG fusion deep learning can achieve accurate, noninvasive CO estimation and that CI-based normalization enhances model agreement with pulmonary artery catheter measurements, supporting continuous catheter-free hemodynamic monitoring. Full article

This paper presents a compact, dual-band, dual-polarization button antenna for Wireless Body Area Networks (WBANs) that operates in the 2.45 GHz and 5.8 GHz Industrial, Scientific, and Medical (ISM) bands. The antenna is engineered in the lower band from 2.33 to 2.8 GHz (18.3% fractional bandwidth) as a linearly polarized, top-loaded monopole, which provides an omnidirectional radiation pattern for on-body communication. In contrast, it functions as a cross-dipole in the higher band, achieving a fractional bandwidth of 66.4% (4.8–9.57 GHz) and a 3 dB axial ratio (AR) bandwidth of 57.4%, producing a broadside radiation with circular polarization for off-body communications. Prototype measurements in both free-space and on-body settings confirm the antenna’s robust performance, successfully validating its dual-band operation, dual-polarization characteristics. Furthermore, Specific Absorption Rate (SAR) simulations conducted on a human model demonstrate that the values are significantly below the established safety limits, confirming the antenna’s suitability for practical wearable applications. Full article

The increasing demand for real-time, energy-efficient, and interference-resilient communication in smart healthcare environments has intensified interest in Biomedical Internet of Things (Bio-IoT) systems. However, ensuring reliable wireless connectivity for wearable and implantable biomedical sensors remains a challenge due to mobility, latency sensitivity, power constraints, and multi-user interference. This paper addresses these issues by proposing a reconfigurable massive multiple-input multiple-output (MIMO) antenna architecture, incorporating hybrid analog–digital beamforming and adaptive signal processing. The methodology combines conventional algorithms—such as Least Mean Square (LMS), Zero-Forcing (ZF), and Minimum Variance Distortionless Response (MVDR)—with a novel mobility-aware beamforming scheme. System-level simulations under realistic channel models (Rayleigh, Rician, 3GPP UMa) evaluate signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), energy efficiency, outage probability, and fairness index across varying user loads and mobility scenarios. Results show that the proposed hybrid beamforming system consistently outperforms benchmarks, achieving up to 35% higher throughput, a 65% reduction in packet drop rate, and sub-10 ms latency even under high-mobility conditions. Beam pattern analysis confirms robust nulling of interference and dynamic lobe steering. This architecture is well-suited for next-generation Bio-IoT deployments in smart hospitals, enabling secure, adaptive, and power-aware connectivity for critical healthcare monitoring applications. Full article

A novel dual-band dual-mode wearable button antenna for wireless body area network (WBAN) applications is proposed in this paper. The antenna ingeniously integrates a monopole structure and an optimized planar inverted-F antenna (PIFA) configuration in a shared radiator, enabling dual-mode operation with a compact size. In the low-frequency band, the monopole structure generates an omnidirectional radiation pattern, facilitating efficient on-body communication. Meanwhile, the PIFA structure in the high-frequency band exhibits directed radiation, optimizing off-body communication. To enhance bandwidth, a parasitic structure is incorporated into the design. Both numerical simulations and experimental measurements are conducted to evaluate the antenna’s bandwidth and radiation performance in free space and on-body environments, with results showing excellent agreement. The measured bandwidth of the antenna on the human tissue is 300 MHz (2.3–2.6 GHz) in the low-frequency band and 4.5 GHz (5.5–10 GHz) in the high-frequency band. The maximum radiation efficiency reaches 76% in the low band (2.4–2.4835 GHz) and 93% in the upper band (5.725–5.875 GHz). Additionally, the peak gain on the human body can achieve 2.5 dB and 6.9 dB for the low and upper bands, respectively. The results confirm that the antenna meets the design requirements for Industrial, Scientific, and Medical (ISM) band applications, making it a promising candidate for WBAN systems. Full article

Air pollution is a major environmental and public health challenge, especially in urban areas where fine-grained air quality data are essential to effective interventions. Traditional monitoring networks, while accurate, often lack spatial resolution and public engagement. This study presents a novel wearable wireless sensor node (WSN) that was developed within the Horizon Europe SOCIO-BEE project to support air quality monitoring through citizen science (CS). The low-cost, body-mounted WSN measures NO₂, O₃, and PM_2.5. Three pilot campaigns were conducted in Ancona (Italy), Maroussi (Greece), and Zaragoza (Spain), and involved diverse user groups—seniors, commuters, and students, respectively. PM_2.5 sensor data were validated through two approaches: direct comparison with reference stations and spatial clustering analysis using K-means. The results show strong correlation with official PM_2.5 data (R² = 0.75), with an average absolute error of 0.54 µg/m³ and a statistical confidence interval of ±3.3 µg/m³. In Maroussi and Zaragoza, where no reference stations were available, the clustering approach yielded low intra-cluster coefficients of variation (CV = 0.50 ± 0.40 in Maroussi, CV = 0.28 ± 0.30 in Zaragoza), indicating that the measurements had high internal consistency and spatial homogeneity. Beyond technical validation, user engagement and perceptions were evaluated through pre-/post-campaign surveys. Across all pilots, over 70% of participants reported satisfaction with the system’s usability and inclusiveness. The findings demonstrate that wearable low-cost sensors, when supported by a structured engagement and data validation framework, can provide reliable, actionable air quality data, empowering citizens and informing evidence-based environmental policy. Full article

The parameters of electromagnetic emissions from the antenna of a wearable radio communication module (parameterizing device functionality) were investigated at different positions near the body where an antenna is located. The specific absorption rate (SAR) coefficient was also investigated as a way of parameterizing the absorption of electromagnetic radiation in the user’s body adjacent to the antenna in various locations. The modeled exposure scenarios concerned a body-worn device with a 2.45 GHz ISM band antenna (used, e.g., for Wi-Fi 2G/Bluetooth applications). The antennas were modeled as follows: (1) located directly on the body (considered to be a model of a disposable, adhesive device) or (2) next to the body (considered to be a model of a classic, reusable, wearable electronic device located inside a plastic housing). Several body sections adjacent to the antenna were considered: head, arm, forearm, and chest (simplified and anatomical body models were used). The numerical models of the exposure scenarios were verified by relevant laboratory tests using physical models. It was found that the use of simplified models of the human body (numerical or physical) may be sufficient when analyzing antenna performance and SAR in a user’s body, such as in studies regarding microwave imaging and sensing, wireless implantable devices, wireless body-area networks or SAR estimation. Full article

This paper presents a novel design of a compact dual-band dual-mode wearable antenna. The antenna is fed through a single coaxial feed probe, which excites TM₀₁ and TM₁₁ modes at 2.45 GHz and 5.8 GHz, respectively. These modes exhibit distinct radiation characteristics. The omnidirectional TM₀₁ mode at 2.45 GHz is suitable for on-body communication, while the directional TM₁₁ mode at 5.8 GHz is more appropriate for off-body communication. The antenna prototype was fabricated and measured. The measured performance is consistent with the simulations. Additionally, further simulations and measurements were conducted to verify the interactions between the proposed antenna and the human body. The results demonstrate that the proposed antenna exhibits significant potential as a candidate for wireless body area network (WBAN) communications. Full article

A circularly polarized (CP) textile antenna is investigated for concurrent on- and off-body wireless communications in the 2.38 GHz medical body area network and 5.8 GHz industrial, scientific, and medical bands in the wireless body area network. The proposed scheme consists of a square microstrip patch antenna (MPA), in which four shorting pins are employed to tune the two resonate modes of TM₁₀ and TM₀₀. Notably, the slant corners on MPA are cut symmetrically to realize unidirectional CP radiation, enabling off-body communication. Moreover, four rotating L-shaped parasite elements are loaded to excite the horizontal polarization mode (TM_hp), which is combined with the TM₀₀ mode to implement CP omnidirectional radiation along the human body. For verification, a proof-of-concept prototype with the dimensions of 45 mm × 45 mm × 2 mm was fabricated and characterized. The measured −10 dB impedance bandwidths of 2.5% and 6.7%, the 3 dB AR bandwidths of 2.5% and 2.7%, and the maximum realized gains of −2.8 and 6.8 dBic are achieved in dual bands, respectively. The experimental tests, such as human body loading, structural deformation, and humidity variation, were carried out. In addition, the wireless communication capability was measured and the radiation safety is evaluated. These performances show that the proposed antenna is an appropriate choice for multifunctional wearable applications. Full article

Recent developments in sensors, wireless communications, and data processing technologies are the main drivers for adopting the Internet of Things (IoT) in healthcare systems. IoT-based healthcare systems can enhance the quality of life significantly and help prevent the occurrence of health problems and epidemics. Deploying IoT-based healthcare on a massive scale raises several issues and challenges. One of the main challenges is the management of the end-to-end network connections of the IoT-based healthcare system. This paper presents a comprehensive survey of smart network management protocols that improve IoT-based healthcare efficiency, ensuring real-time monitoring, secure data transmission, and effective device management. Moreover, a reference architecture has been proposed for the network management of IoT-based smart healthcare systems to ensure the sustainability of service delivery to patients and caregivers. The architecture avoids health-related risks and anomalies by incorporating proper network management techniques and operational requirements pertaining to smart healthcare systems. This paper also discusses architectural implementation insights supported by new technologies such as software-defined networking (SDN) and deep learning (DL). Finally, this paper explores emerging paradigms to advance next-generation network management protocols for future smart healthcare systems. Full article

The development of wearable sensor devices brings significant benefits to patients by offering real-time healthcare via wireless body area networks (WBANs). These wearable devices have gained significant traction due to advantageous features, including their lightweight nature, comfortable feel, stretchability, flexibility, low power consumption, and cost-effectiveness. Wearable devices play a pivotal role in healthcare, defence, sports, health monitoring, disease detection, and subject tracking. However, the irregular nature of the human body poses a significant challenge in the design of such wearable systems. This manuscript provides a comprehensive review of recent advancements in wearable and flexible smart sensor devices that can support the next generation of such sensor devices. Further, the development of direct ink writing (DIW) and direct writing (DW) methods has revolutionised new high-resolution integrated smart structures, enabling the design of next-generation soft, flexible, and stretchable wearable sensor devices. Recognising the importance of keeping academia and industry informed about cutting-edge technology and time-efficient fabrication tools, this manuscript also provides a thorough overview of the latest progress in various fabrication methods for wearable sensor devices utilised in WBAN and their evaluation using body phantoms. An overview of emerging challenges and future research directions is also discussed in the conclusion. Full article