Search Results (95)

This paper explores the potential of amorphous and nanocrystalline glass-coated microwires as highly versatile, miniaturized sensing elements, leveraging their intrinsic nonlinear magnetization dynamics. In magnetic systems, this approach is particularly advantageous because the degree of nonlinearity can be externally tuned using stimuli such as applied magnetic fields, mechanical stress, or temperature variations. From this context, we summarize key properties of microwires—including bistability, a specific easy magnetization direction, internal stress distributions, and magnetostriction—that can be tailored through composition and annealing. In this review, we compare for the first time two key contactless readout methodologies: (i) time-domain detection of the switching field and (ii) frequency-domain harmonic analysis of the induced voltage. These principles have been successfully applied to a broad range of practical sensors, including devices for monitoring mechanical stress in structural materials, measuring temperature in biomedical settings, and detecting magnetic particles. Together, these advances highlight the potential of microwires for embedded, wireless sensing in both engineering and medical applications. Full article

This study presents the design and validation of an open-source framework for biomedical signal acquisition and interoperable data exchange based on the Health Level Seven—Fast Healthcare Interoperability Resources (HL7 FHIR) standard. The proposed system enables secure, wireless transmission of physiological data from distributed sensing nodes toward a locally hosted monitoring platform. The hardware architecture integrates ESP32-WROOM-32 microcontrollers for multi-parameter acquisition, the MQTT protocol for low-latency communication, and a Home Assistant (Nabu Casa, San Diego, CA, USA)–InfluxDB (InfluxData, San Francisco, CA, USA)–Grafana (Grafana Labs, New York, NY, USA) stack for real-time visualization. The novelty of this work lies in the full-stack implementation of HL7 FHIR Observations within a reproducible, open-source environment, ensuring semantic interoperability without reliance on proprietary middleware or cloud services. A case study involving multi-sensor acquisition of electrocardiographic (ECG), photoplethysmographic (PPG), temperature, and oxygen saturation signals was conducted to evaluate system performance. Validation results confirmed consistent end-to-end data flow, sub-second latency, zero packet loss, and accurate semantic preservation across all processing stages. These findings demonstrate the feasibility of implementing standardized, open, and scalable biomedical Internet of Medical Things (IoMT) systems using non-proprietary components. The proposed framework provides a reproducible foundation for future telemedicine and continuous patient-monitoring applications, aligning with FAIR data principles and the ongoing digital transformation of healthcare. Full article

The development of Micro-electro-magnetic Vibration Energy Harvesters (MEMVEHs) plays a crucial role in advancing self-powered nanophotonic, nanoelectronic, and nanosensor systems. As energy autonomy becomes critical for miniaturized devices, MEMVEHs offer a sustainable power source for low-power nanodevices operating in wireless sensor networks, wearable electronics, and biomedical implants. This study provides a comparative assessment of MEMVEH technologies and evaluates their integration potential within next-generation nanoscale systems, enabling enhanced performance, longevity, and energy efficiency of emerging nanotechnologies. Electromagnetic vibration energy harvesters (EMEHs) based on microelectromechanical system (MEMS) technology are promising solutions for powering small-scale, autonomous electronic devices. In this study, two electromagnetic vibration energy harvesters based on microelectromechanical (MEMS) technology are presented. Two models with distinct vibration structures were designed and fabricated. A permanent magnet is connected to a silicon vibration structure (resonator) and a tiny wire-wound coil as part of the energy harvester. The coil has a total volume of roughly 0.8 cm³. Two energy harvesters with various resonators are tested and compared. Model A’s maximum load voltage is 163 mV, whereas Model B’s is 208 mV. A maximum load power of 59.52 μW was produced by Model A at 347 Hz across a 405 Ω load. At 311.4 Hz, Model B produced a maximum load power of 149.13 μW while accelerating by 0.4 g. Model B features a larger working bandwidth and a higher output voltage than Model A. Model B performs better than Model A in comparable experimental settings. Simple study revealed that Model B’s electromagnetic energy harvesting produced superior outcomes. Additionally, it indicates that a nonlinear spring may be able to raise the output voltage and widen the frequency bandwidth. Full article

This study investigated the transformative potential of Compressive Sensing (CS) for optimizing multimodal biomedical signal fusion in Wireless Body Sensor Networks (WBSN), specifically targeting challenges in data storage, power consumption, and transmission bandwidth. Through a Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) following the PRISMA protocol, significant advancements in adaptive CS algorithms and multimodal fusion have been achieved. However, this research also identified crucial gaps in computational efficiency, hardware scalability (particularly concerning the complex and often costly adaptive sensing hardware required for dynamic CS applications), and noise robustness for one-dimensional biomedical signals (e.g., ECG, EEG, PPG, and SCG). The findings strongly emphasize the potential of integrating CS with deep reinforcement learning and edge computing to develop energy-efficient, real-time healthcare monitoring systems, paving the way for future innovations in Internet of Medical Things (IoMT) 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

Electrospun nanofiber-based triboelectric nanogenerators (TENGs) have emerged as a highly promising class of self-powered sensors for a broad range of applications, particularly in intelligent sensing technologies. By combining the advantages of electrospinning and triboelectric nanogenerators, these sensors offer superior characteristics such as high sensitivity, mechanical flexibility, lightweight structure, and biocompatibility, enabling their integration into wearable electronics and biomedical interfaces. This review presents a comprehensive overview of recent progress in electrospun nanofiber-based TENGs, covering their working principles, operating modes, and material composition. Both pure polymer and composite nanofibers are discussed, along with various electrospinning techniques that enable control over morphology and performance at the nanoscale. We explore their practical implementations in both contact-type and non-contact-type sensing, such as human–machine interaction, physiological signal monitoring, gesture recognition, and voice detection. These applications demonstrate the potential of TENGs to enable intelligent, low-power, and real-time sensing systems. Furthermore, this paper points out critical challenges and future directions, including durability under long-term operation, scalable and cost-effective fabrication, and seamless integration with wireless communication and artificial intelligence technologies. With ongoing advancements in nanomaterials, fabrication techniques, and system-level integration, electrospun nanofiber-based TENGs are expected to play a pivotal role in shaping the next generation of self-powered, intelligent sensing platforms across diverse fields such as healthcare, environmental monitoring, robotics, and smart wearable systems. Full article

In the context of the relentless pursuit of precision, intelligence, and personalization within the realm of medical technology, the real-time monitoring of human physiological signals has assumed heightened significance. Implantable wireless sensor devices have exhibited extraordinary capabilities in tracking internal physiological parameters, including intraocular pressure, blood glucose levels, electrocardiographic activity, and arterial blood pressure. These devices are characterized by elevated temporal continuity and exceptional measurement accuracy. This paper undertakes an in-depth investigation into the key technologies underlying biodegradable implantable sensing devices. Initially, it expounds on diverse sensing mechanisms employed in implantable devices. Additionally, it presents common data transmission and power supply strategies for wireless sensing systems. Finally, it introduces biodegradable materials suitable for human implantation and their respective application domains and enumerates several implantable devices that are either under development or have already been commercialized. Through an in-depth and comprehensive discourse on the current state of development and extant challenges in this domain, the development trajectory of biodegradable devices is put forward. Moreover, this paper also serves as a valuable reference for the design and selection of implantable medical devices. Full article

The increasing demand for sustainable and renewable energy solutions has made radio frequency energy harvesting (RFEH) a promising technique for powering low-power electronic devices. RFEH captures ambient RF signals from wireless communication systems, such as mobile networks, Wi-Fi, and broadcasting stations, and converts them into usable electrical energy. This approach offers a viable alternative for battery-dependent and hard-to-recharge applications, including streetlights, outdoor night/security lighting, wireless sensor networks, and biomedical body sensor networks. This article provides a comprehensive review of the RFEH techniques, including state-of-the-art rectenna designs, energy conversion efficiency improvements, and multi-band harvesting systems. We present a detailed analysis of recent advancements in RFEH circuits, impedance matching techniques, and integration with emerging technologies such as the Internet of Things (IoT), 5G, and wireless power transfer (WPT). Additionally, this review identifies existing challenges, including low conversion efficiency, unpredictable energy availability, and design limitations for small-scale and embedded systems. A critical assessment of current research gaps is provided, highlighting areas where further development is required to enhance performance and scalability. Finally, constructive recommendations for future opportunities in RFEH are discussed, focusing on advanced materials, AI-driven adaptive harvesting systems, hybrid energy-harvesting techniques, and novel antenna–rectifier architectures. The insights from this study will serve as a valuable resource for researchers and engineers working towards the realization of self-sustaining, battery-free electronic systems. Full article

This article introduces a microwave sensor tailored for skin hydration monitoring. The design enables wireless operation by separating the sensing component from the reader, making it ideal for wearable devices like wristbands. The sensor consists of a semi-lumped $LC$ resonator coupled to an inductive coil reader, where the capacitive part of the sensing tag is in contact with the skin. The variations in the skin hydration level alter the dielectric properties of the skin, which, in turn, modify the resonances of the $LC$ resonator. Experimental in vivo measurements confirmed the sensor’s ability to distinguish between four hydration conditions: wet skin, skin treated with moisturizer, untreated dry skin, and skin treated with Vaseline, by measuring the resonance frequencies of the sensor. Measurement of the input reflection coefficient ($S_{11}$) using a vector network analyzer (VNA) revealed distinct reflection poles and zeros for each condition, demonstrating the sensor’s effectiveness in detecting skin hydration levels. The sensing principle was analyzed using an equivalent circuit model and validated through measurements of a fabricated sensor prototype. The results confirm in vivo skin hydration monitoring by detecting frequency shifts in the reflection response within the 50–200 MHz range. The measurements and data analysis show less than $0.037\%$ error in transmission zero ($f_z$) together with less than $1.5\%$ error in transmission pole ($f_p$) while being used to detect skin hydration status on individual human subjects. The simplicity of the detection method, focusing on key frequency shifts, underscores the sensor’s potential as a practical and cost-effective solution for non-invasive skin hydration monitoring. This advancement holds significant potential for skincare and biomedical applications, enabling detection without complex signal processing. Full article

Epidermal sensors are pivotal components of next-generation wearable technologies. They offer transformative potential in health monitoring, motion tracking, and biomedical applications. This potential stems from their ultra-thin design, skin compatibility, and ability to continuously detect physiological signals. The long-term functionality relies on advanced power systems balancing flexibility, energy density, and environmental resilience. This review highlights four key power strategies: chemical batteries, biofuel cells, environmental energy harvesters, and wireless power transfer. Breakthroughs in multidimensional materials address challenges in ion transport, catalytic stability, and mechanical durability. Structural innovations mitigate issues like dendrite growth and enzyme degradation. These systems enable applications spanning biomarker analysis, motion sensing, and environmental monitoring. By integrating these advancements, this review concludes with a prospective outlook on future directions for epidermal sensor power systems. Full article

The integration of multi-source energy harvesting (EH) systems into silicon presents a promising avenue for powering autonomous, low-power devices, particularly in applications such as the Internet of Things (IoT), biomedical implants, and wireless sensor networks, where power efficiency and small-size solutions are crucial. This review provides a detailed technical assessment of energy harvesting schemes—including photovoltaic, mechanical, thermoelectric, and radio frequency energy harvesting—and the integration of their associated electronic circuits into silicon integrated solutions. The EH systems are critically analyzed based on their architectures, the number and type of input sources, and key performance metrics such as energy conversion efficiency, output power delivered to loads, silicon area footprint, and degree of integration (e.g., reliance on external components). By examining current advancements and practical implementations, crucial design parameters are assessed for state-of-the-art integrated silicon energy harvesting systems. Furthermore, based on current trends, future research directions are outlined to enhance EH efficiency, reliability, and scalability, paving the way for fully integrated silicon-based EH systems for the next-generation self-powered electronic devices. Full article

The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system’s modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, photoplethysmography, and EKG, to allow for the remote gathering and evaluation of health information. In order to decrease network load and enable the quick identification of abnormalities, edge computing is used for real-time signal filtering and feature extraction. Flexible data transmission based on context and available bandwidth is provided through a hybrid communication approach that includes Bluetooth Low Energy and Wi-Fi. Under typical monitoring scenarios, laboratory testing shows reliable wireless connectivity and ongoing battery-powered operation. The Monit4Healthy system is appropriate for scalable deployment in connected health ecosystems and portable health monitoring due to its responsive power management approaches and structured data transmission, which improve the resiliency of the system. The system ensures the reliability of signals whilst lowering latency and data volume in comparison to conventional cloud-only systems. Limitations include the requirement for energy profiling, distinctive hardware miniaturizing, and sustained real-world validation. By integrating context-aware processing, flexible design, and effective communication, the Monit4Healthy system complements existing IoT health solutions and promotes better integration in clinical and smart city healthcare environments. Full article

The practical application of flexible sensors in sound detection is significantly hindered by challenges such as information isolation, fragmentation, and low fidelity. To address these challenges, this work developed a composite hydrogel via a one-pot method, employing polyvinyl alcohol (PVA) as the first network, polyacrylic acid (PAA) as the second network, and two-dimensional nanomaterials—reduced graphene oxide (rGO)—generated through the redox reaction of polydopamine (PDA) and graphene oxide (GO) as conductive fillers. The uniformly distributed rGO within the hydrogel forms an efficient conductive network, endowing the material with high sensitivity (GF = 0.64), excellent conductivity (8.15 S m⁻¹), rapid response time (350 ms), and outstanding stability. The synergistic interaction between PDA and PAA modulates the hydrogel’s adhesion (0.89 kPa), enabling conformal attachment to skin surfaces. The designed rGO@PVA-PAA hydrogel-based flexible sensor effectively monitors vibrations across diverse frequencies originating from five vocal cavities (head, nasal, oral, laryngeal, and thoracic cavities) during singing. Integrated with multi-position synchronization and Bluetooth wireless sensing technologies, the system achieves coordinated and efficient monitoring of multiple vocal cavities. Furthermore, the hydrogel sensor demonstrates versatility in detecting physiological signals, including electrocardiograms, subtle vibrations, and multi-scale body movements, highlighting its broad applicability in biomedical and motion-sensing applications. Full article

Wireless surface electromyography (sEMG) sensors are very practical in that they can be worn freely, but the radio link between them and the receiver might cause unpredictable latencies that hinder the accurate synchronization of time between multiple sensors, which is an important aspect to study, e.g., the correlation between signals sampled at different sites. Moreover, to minimize power consumption, it can be useful to design a sensor with multiple clock domains so that each subsystem only runs at the minimum frequency for correct operation, thus saving energy. This paper presents the design, implementation, and test results of an sEMG sensor that uses Bluetooth Low Energy (BLE) communication and operates in three different clock domains to save power. In particular, this work focuses on the synchronization problem that arises from these design choices. It was solved through a detailed study of the timings experimentally observed over the BLE connection, and through the use of a dual-stage filtering mechanism to remove timestamp measurement noise. Time synchronization through three different clock domains (receiver, microcontroller, and ADC) was thus achieved, with a resulting total jitter of just 47 µs RMS for a 1.25 ms sampling period, while the dedicated ADC clock domain saved between 10% to 50% of power, depending on the selected data rate. Full article

Magnetoelectric (ME) devices combining piezoelectric and magnetostrictive materials have emerged as powerful tools to miniaturize and enhance sensing and communication technologies. This paper examines recent developments in bulk acoustic wave (BAW) and surface acoustic wave (SAW) ME devices, which demonstrate unique capabilities in ultra-sensitive magnetic sensing, compact antennas, and quantum applications. Leveraging the mechanical resonance of BAW and SAW modes, ME sensors achieve the femto- to pico-Tesla sensitivity ideal for biomedical applications, while ME antennas, operating at acoustic resonance, allow significant size reduction, with high radiation gain and efficiency, which is suited for bandwidth-restricted applications. In addition, ME non-reciprocal magnetoacoustic devices using hybrid magnetoacoustic waves present novel solutions for RF isolation, which have also shown potential for the efficient control of quantum defects, such as negatively charged nitrogen-vacancy (NV⁻) centers. Continued advancements in materials and device structures are expected to further enhance ME device performance, positioning them as key components in future bio-sensing, wireless communication, and quantum information technologies. Full article