A wireless, wearable, battery-free multi-sensory system is essential for continuous, non-invasive real-time monitoring of multiple physiological parameters, enabling seamless, discreet healthcare. The main bottleneck in developing such systems lies in achieving low power consumption to enable battery-free operation, while maintaining reliable, high-frequency data acquisition and efficient wireless communication with a skin-impedance-matched antenna within a compact, wearable form factor. To overcome this, we integrate energy-harvesting technologies with high-precision multiple sensors and a flexible, skin-compatible antenna system into a single platform, enabling battery-free operation with efficient data transmission and reception. Our multi-sensory system experimentally demonstrates successful skin-mountable monitoring of ECG, SpO 2 , and temperature at a sampling rate of 70 Hz, with data wirelessly transmitted via Bluetooth Low Energy, all powered by a radio-frequency energy-harvesting antenna. Beyond personal health tracking, this technology also holds great potential for remote patient monitoring in chronic disease management, empowering healthcare providers with continuous access to real-time patient data for timely and uninterrupted data acquisition. Typically, health monitoring systems depend on separate hardware for each physiological signal—such as electrocardiogram, pulse oximetry, and temperature—which requires bulky setups with complex wiring, limiting their practicality for wearable and continuous use. These limitations significantly hinder proactive and long-term monitoring beyond clinical settings. Here, a fully integrated, skin-mountable multisensory patch designed for chest application is demonstrated. The system simultaneously acquires electrocardiogram (ECG), pulse oximetry, and temperature signals, powered sequentially via a battery-free near-field communication (NFC) antenna. Furthermore, high-frequency, noise-free data transmission is achieved through a skin-impedance-matched flexible Bluetooth antenna, ensuring seamless communication without compromising user comfort. This compact, wireless system makes it possible to monitor vital physiological parameters remotely and in real time, helping with early diagnosis, ongoing care, and preventive health tracking outside clinical environments. Fig. | Conceptual illustration of a battery-free, skin-mountable wearable patch. The system enables simultaneous energy harvesting and data transmission through an integrated flexible NFC and Bluetooth antenna. • Integration of Electrocardiogram, pulse oximetry, and temperature sensing into a single flexible and skin-conformal patch for comprehensive physiological monitoring. • Battery-free operation through sequential powering enabled by an embedded NFC antenna for wireless energy harvesting. • Flexible Bluetooth antenna matched to skin impedance for robust and noise-free wireless transmission in real-time. • Optimized system for short-range wireless data transfer (1–10 m), enabling real-time smartphone visualization and cloud connectivity. • Demonstration of consistent signal quality across ECG, SpO₂, and temperature, benchmarked against commercial devices.

A portable multiplexed electrochemical biosensor for lactate and pH monitoring in calf saliva.

In wireless communication, the multipath effect and the time-varying channel due to mobility will directly lead to the key update cycle lagging far behind the channel change, which is difficult to effectively resist various malicious attacks and stealing behaviors, and affects the effect of privacy data protection in wireless communication. To this end, a deep learning-based dual chaos encryption method is proposed for wireless communication privacy data. Combining the chaotic characteristics of one-dimensional Logistic mapping and two-dimensional Henon mapping, the dual chaotic key is generated to extend the key space and improve the anti-attack ability; and the bidirectional long and short-term memory network (BiLSTM) is used to analyze the data such as key usage records, accurately predict the timing of the key updating, and generate a new key when anomalies are detected, and then distribute it securely. Taking the updated double chaotic key as input, the AES algorithm is used to realize wireless communication privacy data encryption through key expansion, initial round encryption, multiple rounds of iterative encryption and final round encryption, while the decryption process restores the plaintext by inverse operation. Experiments demonstrate that the method can effectively realize wireless communication privacy data encryption, and the security index can reach more than 0.94 in the face of different types of network attacks. It demonstrates that the proposed method can have the ability to resist all kinds of attacks and protect the security of private data.

The rapid expansion of wireless data traffic is placing increasing strain on the energy consumption of current communication networks, intensifying the tension between performance and sustainability objectives. In interference-intensive multiple access scenarios such as power-domain non-orthogonal multiple access (NOMA), energy-efficient optimization is particularly challenging due to the strong coupling between power control and resource allocation decisions. In order to solve this issue, this paper introduces an AI-Enhanced Energy Optimization Framework (AEEOF), which uses deep spatio-temporal learning and reinforcement learning to provide adaptive and energy-aware network control. The proposed framework incorporates a Spatio-Temporal Graph Convolutional Network (ST-GCN) to learn spatial interference relationships and a Gated Recurrent Unit (GRU) to capture temporal traffic dynamics, embedding the resulting representations into a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) controller to support sequential decision-making. Such a design provides the framework with the ability to dynamically distribute power and timing policies based on changes in network conditions. Extensive simulations in a realistic 5G-oriented environment of high interference levels prove the significant performance improvement. The suggested solution can save up to 15% of energy and make the energy use more efficient by about 40%, which is the number of bits delivered per joule. The overall system throughput goes up by 6.25%, the cell-edge user data rate goes up by up to 60%, the fairness goes up by 20%, and the chance of an outage goes down by 70%. A systematic ablation study with three architectural variants validates the individual contribution of each core component - the ST-GCN spatial module, the GRU temporal module, and the MADDPG reinforcement learning controller. Comparative evaluation against conventional orthogonal and non-AI-assisted baselines further supports the effectiveness of the proposed framework within the studied setting. These findings indicate that intelligent spatio-temporal learning is a promising approach for improving energy efficiency and network performance in interference-intensive wireless environments, as demonstrated within the studied 5G-oriented simulation setting.

Homomorphic threshold detection for wireless sensor data using a privacy-preserving machine learning model

Effective management of prenatal nutrient concentrations, such as those associated with folate, is critical for the health of the prospective mother and child, but quantifying them currently requires frequent blood tests and specialized laboratories. Human sweat is a non-invasive alternative to blood that is well suited for point-of-care biosensing. Here we present a skin-interfaced microcapsule that enables collection and storage of pristine, microlitre volumes of sweat and supports an efficient interface to a portable lab-on-a-disc platform for folate quantification. This platform automates an entire enzyme-linked immunoassay sequence for measuring folate in sweat, including incubation, washing, mixing and detection, and facilitates wireless data transmission. A series of tests in human participants reveal a dose-response relationship between oral intake of folate supplements and sweat folate levels, with a strong correlation in levels between sweat and serum. In addition, daily tracking of sweat folate concentrations shows clear differences between control periods without supplementation and daily intake periods. This technology creates possibilities for the routine use of sweat for precise point-of-care assessment of prenatal nutrient bioavailability.

Electrical stimulation of neural circuits is expanding therapeutic strategies to modulate brain, autonomic, and immune functions. Devices delivered endovascularly offer a less invasive alternative to conventional implanted electrodes, while delivering spatio-temporal specificity superior to noninvasive techniques. We demonstrate a fully endovascular sub-1-mm3 implant, utilizing ultrasound for wireless power delivery and data telemetry in a fashion invariant to device orientation. The implant consists of piezoelectric transducers, an energy storage capacitor, an application-specific integrated circuit, and electrodes packaged on a 7-um-thick polyimide scaffold. The implant can be delivered through a microcatheter in a manner analogous to conventional neurovascular stents, and self-expands upon deployment to appose the vessel walls. We demonstrate intravascular stimulation of the autonomic nervous system from the carotid artery, achieving modulation of blood pressure in rabbits. This approach establishes a broadly applicable platform for neural interfaces enabling both stimulation and recording.

A multi-band simultaneous wireless power and data transfer system using multiple relay coils

Physiological palpation serves as a primary clinical modality for identifying pathological changes in tissue compliance. However, its diagnostic precision is inherently limited by the subjective nature of human haptic perception and the lack of quantifiable mechanical metrics. This work describes a bio-inspired, portable tactile interface engineered for the non-invasive and real-time characterization of tissue stiffness. The system incorporates multimodal piezoresistive sensing elements that emulate the specific mechanotransduction functions of cutaneous receptors, namely Merkel disks and Ruffini endings. By integrating Hertzian contact mechanics to decouple pressure and strain signals, the platform analytically derives the effective Young's modulus of heterogeneous soft tissues. The developed sensor architecture exhibits a functional range of 0-600 kPa and a gauge factor of 10.8, facilitating high-fidelity detection of subcutaneous anomalies. Validation against various nodule geometries and depths demonstrates that the system achieves a diagnostic resolution surpassing conventional manual assessments. Furthermore, the integration of wireless data processing enables instantaneous, on-site mechanical profiling. This platform provides a scalable framework for objective diagnostics, robotic haptics, and continuous physiological monitoring, establishing a robust bridge between qualitative clinical observation and quantitative biomechanical analysis.

Continuous respiratory monitoring remains one of the most critical yet unmet needs in personal and clinical healthcare. To overcome the limitations of cumbersome systems and motion-prone wearables, we present a moisture-gated bio-semiconductor-based electronic tattoo (BSET) that enables imperceptible, skin-conformable, and wireless respiratory monitoring by leveraging the hydration-sensitive ionic-electronic conductivity of melanin and the skin-compatibility of silk. Fabricated from a silk fibroin-melanin nanofiber bio-composite, the BSET is ultrathin at 18 μm and highly breathable, achieving a water vapor transmission rate exceeding 3000 g·m-2·d-1. By detecting exhaled moisture directly on the philtrum, the sensor exhibits a rapid response time of 1 s and a recovery time between 2 and 10 s. A direct-spun nanofiber-based wiring strategy ensures robust integration, accommodating a 10 mm displacement under 10.4 MPa of stress without failure. Driven by a compact 3 g circuit with 20 mW of power consumption, the system supports continuous wireless data transmission for over 7.3 h. During vigorous exercise and sleep, the BSET reliably monitored respiratory dynamics, identifying 10-20 s apnea events and enabling multiparameter analysis of breathing frequency and exhalation intensity. This lightweight system establishes a scalable and clinically relevant solution for continuous respiratory surveillance.

Centrifugal microfluidics has emerged as a promising platform for automated bioanalysis, such as nucleic acid testing and immunoassays. In particular, real-time centrifugal microfluidic PCR provides a highly effective solution for point-of-care molecular diagnostics in resource-limited settings. However, during the development of these systems, monitoring their operational state for evaluation or calibration is difficult due to the inherent conflicts among thermal, optical, and centrifugal interactions. To address this challenge, this study presents a wireless, in-situ calibration 'chip' with multiple functions for system calibration and evaluation by being operated in a way similar to a normal disk chip. The calibration 'chip' consists of multiple different structural layers, e.g., sensing, optical, fluidic and electrical layers, integrating multiple functions including temperature calibration, fluorescence signal simulation, dynamic fluid monitoring, and mechanical sensing. To allow the in-situ calibration 'chip' to reasonably fit the centrifugation platform, it is powered by embedded rechargeable batteries and meanwhile Bluetooth-based wireless data transmission is adopted. Experimental results demonstrate that the calibration 'chip' is capable of performing multiple different tasks based on comprehensive sensing and actuation mechanisms, which is helpful to conveniently perform in-situ monitoring of the centrifugal microfluidic system with an on hand 'tool'.

Mosquito-borne diseases pose a major public health challenge and require effective, scalable surveillance to guide targeted interventions. Existing monitoring techniques, ranging from manual morphological identification to acoustic, optical, and spectroscopic sensing, remain constrained by environmental sensitivity, labor demands, and limited ground-truth validation. Here, we present a fully autonomous, field-deployable platform, called automated intelligent mosquito sentinel (AIMS), integrating distributed mosquito monitoring outposts (MMOs) and a centralized analysis center (AC) for scalable, non-invasive mosquito surveillance. AIMS employs an adaptive event-triggering mechanism, optimized through feature engineering of colour and texture pairs, to enable energy-efficient detection with zero missed events and a false-positive rate below 1%. At the analytical level, a hierarchical gated residual network performs multitask classification of taxonomy and sex with accuracies of 99.51% at species and 98.02% for sex, demonstrating interpretable, biologically meaningful attention patterns. The self-powered architecture, robust wireless data transmission, and large-scale field dataset underpin reliable operation across diverse ecological settings. Together, these results show that AIMS can support scalable and sustainable mosquito surveillance and may also be useful for broader entomological monitoring and public health applications.

Wireless communication technologies for bioelectronic implants enable remote monitoring for diagnosis and adaptive therapeutic intervention without the constraints of wired connections. However, wireless data uplink from millimeter-scale devices deep in the body struggles to achieve low power consumption while maintaining large misalignment tolerances. Here, we report a passive wireless backscatter communication system based on magnetoelectric transducers that consumes less than 0.3 pJ/bit and achieves less than 1E-6 bit error rate at a distance of 55 mm while tolerating a misalignment of 10 mm. Using this robust data uplink, we designed a wireless cardiac sensing node that can transmit electrocardiogram signals from the beating heart surface of a porcine model to a custom external transceiver using the magnetoelectric backscatter uplink. This reliable, near-zero-power communication method provides opportunities for next-generation bioelectronics to feature real-time physiological monitoring and closed-loop therapies while maintaining a small form factor and low power consumption.

This paper presents an AI-driven multisensor wearable system for real-time breathing pattern recognition by integrating an inertial measurement unit (IMU) and a flex sensor with wireless data connectivity. Three artificial intelligence models-transformer, convolutional neural network-long short-term memory (CNN-LSTM), and histogram gradient boosting (HGB)-were evaluated for breathing pattern recognition across different model complexities (complex, simple, pure) and sensor configurations (IMU , Flex , and combined). The multisensor system combined with AI model was tested with multiple participants. The complex transformer model, trained with focal loss on the combined IMU and flex sensor data, achieved the highest performance, with 93.41% accuracy and a mean area under the curve (AUC) of 0.9919, outperforming all the other models. Multimodal input significantly improved classification accuracy-up to 20% higher than flex sensor models in six-class tasks-while focal loss enhanced robustness, particularly in addressing class imbalance. These results demonstrate the potential of combining wearable sensor fusion with deep learning to enable accurate, noninvasive, and wireless real-time respiratory monitoring, with potential applications in clinical diagnostics, telemedicine, and personalized health tracking.

Closed-loop bioelectronic systems that adapt stimulation to real-time physiological feedback hold transformative potential for treating neurological and cardiac disorders and are emerging as key components of future ultrasonic brain-machine interfaces (uBMIs). Realizing this requires the simultaneous achievement of millimeter‑scale deep-tissue targeting, artifact-free physiological feedback, and robust wireless power and data transfer, which remain elusive with current methods. Here, we present an integrated ultrasonic platform engineered to overcome these fundamental limitations. We propose a physics-constrained metasurface design framework to enable high-resolution multifocal ultrasound energy delivery through highly aberrating biological barriers such as the skull and ribs, achieving improved experimental targeting accuracy (e.g., ±6.5% intensity uniformity across multiple foci). We demonstrate the platform's adaptive stimulation capabilities through two distinct paradigms: attention-based ultrasound stimulation and cardiac-synchronized ultrasound stimulation. Furthermore, we introduce a novel dual-channel acoustic link that enables continuous wireless power and wireless data streaming through the skull with a single acoustic metasurface, demonstrating robustness even with a 400-fold power differential. This integrated ultrasonic framework, providing seamless integration of precise spatial targeting through biological barriers, adaptive physiological feedback, and untethered operation, contributes to the development of next-generation uBMIs and closed-loop bioelectronic therapies.

This study presents a portable and cost-effective brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) to enable non-verbal communication for patients in intensive care units (ICUs). A custom wireless single-channel electroencephalography (EEG) acquisition system was developed to transmit the acquired EEG data via Wi-Fi using an ESP32 microcontroller to a Raspberry Pi processor. The participants interacted with a screen that displayed multiple flickering icons, each corresponding to a predefined message. EEG data were acquired while subjects focused on a target icon, generating a unique SSVEP response. Canonical correlation analysis (CCA), incorporating harmonic frequencies, was used for real-time classification. Twelve healthy participants were involved in the study and the proposed system demonstrated strong performance, yielding a mean accuracy of 98.33% for classification tasks and achieving an average information transfer rate (ITR) of 40.98 bits/min. Detection latency was averaged 3.215 s, with a computation time of 215 ms. Upon classification, an audio message was generated and simultaneously sent to caregivers via the short message service (SMS) and the multimedia messaging service (MMS). This system demonstrates a reliable and efficient single-channel SSVEP-based BCI, integrating real-time processing, wireless data transmission, and multimodal feedback. Its portable design and high accuracy make it highly suitable for practical deployment in ICU settings, providing an effective communication alternative for critically ill, non-verbal patients.

Wireless connectivity is required in modern in-body electronic devices (IEDs), such as in-body sensors. However, their frequent wireless communication operations will consume more energy than those of conventional IEDs. In addition, energy-limited IEDs require concurrent data and power transfer to maintain uninterrupted operation, enabling medical data communication while continuously supplying energy for battery recharging In this paper, we present a joint optical wireless data and power transfer system for IEDs based on single-carrier transmission, focusing on realistic operating scenarios, with particular emphasis on forward telemetry and energy-harvesting performance. . Commercially available components were employed, including a single-beam 850nm NIR LED that simultaneously delivers modulated data and optical energy through biological tissue (i.e., ex vivo porcine tissue samples, a representative biological model for human soft-tissue optical propagation). A photodetector receives forward telemetry signals, and a photovoltaic (PV) cell harvests residual optical power concurrently to charge a supercapacitor via a power management integrated circuit (PMIC). In this study, we also account for a real-life factor, namely the impact of clothing on optical light transmission, which may attenuate the incident light depending on fabric type and thickness. Two textile samples were used in this study, representing a thin fabric with high porosity and a thicker fabric with lower porosity (dense yarn). Energy can be harvested through biological tissue during active optical data transmission, thereby providing supplementary energy support for battery-limited IEDs. However, the presence of cloth led to a noticeable decrease in the harvested energy, even with a thin layer. The slower supercapacitor charging is attributed to attenuation of incident optical power by clothing, thereby reducing the PV cell's output voltage. The study is important for developing future wearable-to-implant links for non-invasive medical applications that account for the presence of clothing.

Miniature implantable neural interface devices are increasingly critical for both neuroscience research and clinical neuromodulation applications. However, device miniaturization imposes stringent constraints on power, area, and performance, creating challenges for implementing energy-efficient neuromodulation, high-fidelity neural recording, and wireless data telemetry. This review provides a comprehensive overview of low-power circuit designs enabling next-generation neural interfaces. We discuss energy-efficient stimulation drivers for optogenetic neuromodulation, highlighting advanced switched-capacitor-based techniques that reduce supply voltage requirements while maintaining high-current LED pulses. Low-noise neural recording frontends, including preamplifier-fronted structures, as well as ΔΣ ADC-based and NS-SAR-based direct-digitizing architectures, are reviewed with emphasis on techniques for dynamic range extension, linearity improvement, and artifact tolerance. Finally, state-of-the-art backscatter-based wireless telemetry methods are presented, covering load-shift keying (LSK), frequency-splitting, and push-pull quadrature modulation approaches that decouple power and data transfer to achieve high data rates with minimal energy consumption. This review highlights the critical role of circuit-level innovations in overcoming the power and performance limitations of miniature implants and provides insights for the design of next-generation neural interface systems.

Intelligent microfluidic device for multiplex detection and prompt warning of upper and lower respiratory tract infections.

Exhaled breath condensate (EBC) analysis, a promising noninvasive respiratory monitoring method, has emerged as a pivotal technique for assessing the health status of patients with respiratory disorders and is widely used in clinical research and daily health management. However, conventional analytical methods face challenges in real-time in situ detection of physicochemical indicators and active EBC collection in a power-free way. Herein, a wearable multimodal detection system (WMDS) with efficient collection and real-time analysis of EBC is developed. Specifically, the WMDS consists of a bio-inspired collector, an electrochemical sensor (EBC analysis), a respiratory sensor (humidity and respiratory rate), a temperature sensor, and a flexible printed circuit board. The miniature-sized collector with a cactus spine-like structure can actively harvest 4.1 μL of EBC within 1 min without power consumption. Leveraging self-developed sensors and wireless data transmission circuitry, the WMDS enables real-time in situ monitoring of multimodal EBC analytes (hydrogen peroxide, nitrite, urea) and respiratory parameters (temperature, humidity, and rate). Remarkably, the WMDS exhibits dual-range detection capability covering both physiological and pathological conditions: the low-concentration range of 0-500 μmol/L is applicable for routine health monitoring and early disease screening, with detection limits (LODs) of 0.209, 0.155, and 0.573 μmol/L and sensitivities of 2.7 × 10-2, 3.4 × 10-2, and 9.0 × 10-3 μA/(μmol/L) for EBC analytes; the high-concentration range exceeding 500 μmol/L is designed for severe pathological condition detection, where LODs and sensitivities are 302.2, 278.7, 325.2 μmol/L and 1.9 × 10-2, 1.3 × 10-2, 3.9 × 10-3nA/(μmol/L) respectively. As a proof-of-concept, the WMDS is applied to on-body respiratory monitoring, validating its potential application in real-time in situ health monitoring.