What proteomics has taught us about honey bee (Apis mellifera) health and disease.

The Western honey bee, Apis mellifera, is currently navigating a gauntlet of environmental pressures, including the persistent threat of parasites, pathogens, and climate change - all of which compromise the vitality of honey bee colonies. The repercussions of their declining health extend beyond the immediate concerns of apiarists, potentially imposing economic burdens on society through diminished agricultural productivity. Hence, there is an imperative to devise innovative monitoring techniques for assessing the health of honey bee populations. Proteomics, recognized for its proficiency in biomarker identification and protein-protein interactions, is poised to play a pivotal role in this regard. It offers a promising avenue for monitoring and enhancing the resilience of honey bee colonies, thereby contributing to the stability of global food supplies. This review delves into the recent proteomic studies of A. mellifera, highlighting specific proteins of interest and envisioning the potential of proteomics to improve sustainable beekeeping practices amidst the challenges of a changing planet.

Self-Powered Hybrid Motion and Health Sensing System Based on Triboelectric Nanogenerators.

Triboelectric nanogenerator (TENG) represents an effective approach for the conversion of mechanical energy into electrical energy and has been explored to combine multiple technologies in past years. Self-powered sensors are not only free from the constraints of mechanical energy in the environment but also capable of efficiently harvesting ambient energy to sustain continuous operation. In this review, the remarkable development of TENG-based human body sensing achieved in recent years is presented, with a specific focus on human health sensing solutions, such as body motion and physiological signal detection. The movements originating from different parts of the body, such as body, touch, sound, and eyes, are systematically classified, and a thorough review of sensor structures and materials is conducted. Physiological signal sensors are categorized into non-implantable and implantable biomedical sensors for discussion. Suggestions for future applications of TENG-based biomedical sensors are also indicated, highlighting the associated challenges.

Ultra-Broadband Flexible Thin-Film Sensor for Sound Monitoring and Ultrasonic Diagnosis.

Small-scale and flexible acoustic probes are more desirable for exquisite objects like human bodies and complex-shaped components than conventional rigid ones. Herein, a thin-film flexible acoustic sensor (FA-TES) that can detect ultra-broadband acoustic signals in multiple applications is proposed. The device consists of two thin copper-coated polyvinyl chloride films, which are stimulated by acoustic waves and contact each other to generate the triboelectric signal. Interlocking nanocolumn arrays fabricated on the friction surfaces are regarded as a highly adaptive spacer enabling this device to respond to ultra-broadband acoustic signals (100 Hz-4 MHz) and enhance sensor sensitivity for film weak vibration. Benefiting from the characteristics of high shape adaptability and ultrawide response range, the FA-TES can precisely sense human physiological sounds and voice (≤10 kHz) for laryngeal health monitoring and interaction in real-time. Moreover, the FA-TES flexibly arranged on a 3D-printed vertebra model can effectively and accurately diagnose the inner defect by ultrasonic testing (≥1 MHz). It envisions that this work can provide new ideas for flexible acoustic sensor designs and optimize real-time acoustic detections of human bodies and complex components.

Contactless face video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network.

Continuous monitoring of vital signs such as respiration and heart rate is essential to detect and predict conditions that may affect the patient's well-being. To detect these vital signs most medical systems use contact sensors. They are not feasible for long term monitoring and are not repeatable. Vital signs using facial video-noncontact monitoring are becoming increasingly important. Researchers in the last few years although considerable progress has been made, challenging datasets absence timing of assessment process and the technology still has some limitations such as time consuming nature and lack of computer portability. To solve those problems, we propose a contactless video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network. In the proposed technique, modified war strategy optimization algorithm is proposed to segment the face portion from the input video frames. Then, we utilize the known data acquisition models to extract vital signs from the segmented face portions are heart rate, blood pressure, respiratory rate and oxygen saturation. An improved neural network structure (Lifting Net) is further used to achieve the adaptive extraction of deep hidden features for specific signs, for realizing the high precision of human health monitoring. The Hughes effect or dimensionality issue affects detection accuracy in sign classification when there are fewer training instances relative to the number of spectral features. The problem can be overcome through feature optimization here Northern goshawk optimization algorithm is used to select optimal best features which reduces the data dimensionality issue. Furthermore, hybrid deep ensemble reinforcement learning classifier is proposed for the human vital sign detection and classification which ensures the early detection of patient abnormality. Finally, we validate our framework using benchmark video datasets such as TokyoTechrPPG, PURE and COHFACE. To proves the effectiveness of proposed technique using simulation results and comparative analysis.

Toward flexible piezoresistive strain sensors based on polymer nanocomposites: a review on fundamentals, performance, and applications.

The fundamentals, performance, and applications of piezoresistive strain sensors based on polymer nanocomposites are summarized herein. The addition of conductive nanoparticles to a flexible polymer matrix has emerged as a possible alternative to conventional strain gauges, which have limitations in detecting small strain levels and adapting to different surfaces. The evaluation of the properties or performance parameters of strain sensors such as the elongation at break, sensitivity, linearity, hysteresis, transient response, stability, and durability are explained in this review. Moreover, these nanocomposites can be exposed to different environmental conditions throughout their lifetime, including different temperature, humidity or acidity/alkalinity levels, that can affect performance parameters. The development of flexible piezoresistive sensors based on nanocomposites has emerged in recent years for applications related to the biomedical field, smart robotics, and structural health monitoring. However, there are still challenges to overcome in designing high-performance flexible sensors for practical implementation. Overall, this paper provides a comprehensive overview of the current state of research on flexible piezoresistive strain sensors based on polymer nanocomposites, which can be a viable option to address some of the major technological challenges that the future holds.

Relationships between pig farm management and facilities and lung lesions' scores and between lung lesions scores and carcass characteristics.

BACKGROUND: The objective of this study was to examine the inter-relationships between pig farm management and facilities (as assessed by questionnaire) and post-mortem lung lesion (lung score assesment), which are the result of respiratory infections. The relationships between carcass characteristics and post-mortem lung lesion scores were also investigated. RESULTS: Questionnaire responses were collected from 22 self-selecting pig farmers about their farm facilities/management and health condition of the respiratory system of pigs, including the occurrence of clinical respiratory signs, results of laboratory testing for respiratory pathogens, and the use of respiratory vaccines. When fatteners were sent to the abattoir, their carcasses (n = 1,976) were examined for evidence of respiratory disease by lung lesion (pleuritis pneumonia-like (PP-like) and enzootic pneumonia-like (EP-like) lesions) scoring and the Actinobacillus pleuropneumoniae Index (APPI) was calculated. Carcass characteristics were recorded and, retrospectively, the prevalence of cachectic pigs was calculated. Using these variables, the relationships between farm facilities/management and lung lesions scores and the relationships between the latter and carcass characteristics and cachexia were explored. The key findings relating farm facilities and management to lung lesions were: slatted floors were associated with significantly higher EP-like lesions scores than litter bedding in weaners, single-stage fattening in the same building was associated with significantly higher EP-like lesions scores than two-stage fattening, but herd size, stocking density, use of all-in/all-out (AIAO) rule, technological break duration and variation in daily temperature did not affect lung lesions scores. The key findings relating lung lesion scores to carcass characteristics were: a significant, negative correlation between EP-like scores and carcass weight but not with other carcass characteristics, a significant positive correlation between PP-like scores and carcass meat content and prevalence of cachectic carcasses and a significant positive correlation between lung APPI and prevalence of cachectic carcasses. CONCLUSIONS: It can be concluded that both farm facilities and management affect lung lesions scores and that the latter affect carcass characteristics. Lung lesion scoring is an inexpensive technique suitable for rapid monitoring of large numbers of carcasses that can be performed after animal slaughter. It provides useful information to inform producers about possible deficits in farm facilities or management and is a predictor of economic loss due to poorer quality carcasses.

Self-powered freestanding multifunctional microneedle-based extended gate device for personalized health monitoring.

Online monitoring of prognostic biomarkers is critically important when diagnosing disorders and assessing individuals' health, especially for chronic and infectious diseases. Despite this, current diagnosis techniques are time-consuming, labor-intensive, and performed offline. In this context, developing wearable devices for continuous measurements of multiple biomarkers from body fluids has considerable advantages including availability, rapidity, convenience, and minimal invasiveness over the conventional painful and time-consuming tools. However, there is still a significant challenge in powering these devices over an extended period, especially for applications that require continuous and long-term health monitoring. Herein, a new freestanding, wearable, multifunctional microneedle-based extended gate field effect transistor biosensor is fabricated for online detection of multiple biomarkers from the interstitial fluid including sodium, calcium, potassium, and pH along with excellent electrical response, reversibility, and precision. In addition, a hybrid powering system of triboelectric nanogenerator and solar cell was developed for creating a freestanding, closed-loop platform for continuous charging of the device's battery and integrated with an Internet of Things technology to broadcast the measurements online, suggesting a stand-alone, stable multifunctional tool which paves the way for advanced practical personalized health monitoring and diagnosis.

Effect of different screen brightness and devices on online visual acuity test.

PURPOSE: This study aimed to study the difference in test results of online visual acuity (VA) test under different devices and screen brightness conditions and to compare online VA test with Early Treatment Diabetic Retinopathy Study (ETDRS). METHODS: Healthy volunteers with the best corrected VA of 0.0 LogMAR or higher were recruited. VAs under ETDRS were tested first, and then online VA test (the Stanford Acuity Test, StAT) visual acuities using iPad Air2 and Microsoft Surface pro4 under 50% and 100% screen brightness were performed. The VA results and the testing times were compared between different devices and screen brightness conditions. RESULTS: A total of 101 eyes were included in this study. The VA results measured by the StAT were better than those of ETDRS. The VA results measured at 100% screen brightness were better than those of 50% brightness (mean difference, 0.013 logMAR at most, less than 1 letter); the VA results measured by iPad Air2 were better than those of Surface pro4 (mean difference, -0.009 logMAR at most, less than 1 letter). Significantly less time was spent on VA testing under StAT than that under ETDRS. CONCLUSION: The impact of screen brightness and the device on the VA results generated by online VA tests was clinically insignificant. In addition, online VA tests are found to be reliable and more time efficient than ETDRS.

Wireless, soft electronics for rapid, multisensor measurements of hydration levels in healthy and diseased skin.

Precise, quantitative measurements of the hydration status of skin can yield important insights into dermatological health and skin structure and function, with additional relevance to essential processes of thermoregulation and other features of basic physiology. Existing tools for determining skin water content exploit surrogate electrical assessments performed with bulky, rigid, and expensive instruments that are difficult to use in a repeatable manner. Recent alternatives exploit thermal measurements using soft wireless devices that adhere gently and noninvasively to the surface of the skin, but with limited operating range (∼1 cm) and high sensitivity to subtle environmental fluctuations. This paper introduces a set of ideas and technologies that overcome these drawbacks to enable high-speed, robust, long-range automated measurements of thermal transport properties via a miniaturized, multisensor module controlled by a long-range (∼10 m) Bluetooth Low Energy system on a chip, with a graphical user interface to standard smartphones. Soft contact to the surface of the skin, with almost zero user burden, yields recordings that can be quantitatively connected to hydration levels of both the epidermis and dermis, using computational modeling techniques, with high levels of repeatability and insensitivity to ambient fluctuations in temperature. Systematic studies of polymers in layered configurations similar to those of human skin, of porcine skin with known levels of hydration, and of human subjects with benchmarks against clinical devices validate the measurement approach and associated sensor hardware. The results support capabilities in characterizing skin barrier function, assessing severity of skin diseases, and evaluating cosmetic and medication efficacy, for use in the clinic or in the home.

Robust, self-adhesive, reinforced polymeric nanofilms enabling gas-permeable dry electrodes for long-term application.

Robust polymeric nanofilms can be used to construct gas-permeable soft electronics that can directly adhere to soft biological tissue for continuous, long-term biosignal monitoring. However, it is challenging to fabricate gas-permeable dry electrodes that can self-adhere to the human skin and retain their functionality for long-term (>1 d) health monitoring. We have succeeded in developing an extraordinarily robust, self-adhesive, gas-permeable nanofilm with a thickness of only 95 nm. It exhibits an extremely high skin adhesion energy per unit area of 159 µJ/cm2 The nanofilm can self-adhere to the human skin by van der Waals forces alone, for 1 wk, without any adhesive materials or tapes. The nanofilm is ultradurable, and it can support liquids that are 79,000 times heavier than its own weight with a tensile stress of 7.82 MPa. The advantageous features of its thinness, self-adhesiveness, and robustness enable a gas-permeable dry electrode comprising of a nanofilm and an Au layer, resulting in a continuous monitoring of electrocardiogram signals with a high signal-to-noise ratio (34 dB) for 1 wk.

The influence of ambient environmental factors on breakthrough Cancer pain: insights from remote health home monitoring and a proposed data analytic approach.

BACKGROUND: Breakthrough cancer pain (BTCP) is primarily managed at home and can stem from physical exertion and emotional distress triggers. Beyond these triggers, the impact of ambient environment on pain occurrence and intensity has not been investigated. This study explores the impact of environmental factors on the frequency and severity of breakthrough cancer pain (BTCP) in the home context from the perspective of patients with advanced cancer and their primary family caregiver. METHODS: A health monitoring system was deployed in the homes of patient and family caregiver dyads to collect self-reported pain events and contextual environmental data (light, temperature, humidity, barometric pressure, ambient noise.) Correlation analysis examined the relationship between environmental factors with: 1) individually reported pain episodes and 2) overall pain trends in a 24-hour time window. Machine learning models were developed to explore how environmental factors may predict BTCP episodes. RESULTS: Variability in correlation strength between environmental variables and pain reports among dyads was found. Light and noise show moderate association (r = 0.50-0.70) in 66% of total deployments. The strongest correlation for individual pain events involved barometric pressure (r = 0.90); for pain trends over 24-hours the strongest correlations involved humidity (r = 0.84) and barometric pressure (r = 0.83). Machine learning achieved 70% BTCP prediction accuracy. CONCLUSION: Our study provides insights into the role of ambient environmental factors in BTCP and offers novel opportunities to inform personalized pain management strategies, remotely support patients and their caregivers in self-symptom management. This research provides preliminary evidence of the impact of ambient environmental factors on BTCP in the home setting. We utilized real-world data and correlation analysis to provide an understanding of the relationship between environmental factors and cancer pain which may be helpful to others engaged in similar work.

Systematic design of health monitoring systems centered on older adults and ADLs.

BACKGROUND: Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life. METHOD: A design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain. RESULTS: The results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning. CONCLUSIONS: The defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.

Prevalence, Antimicrobial Resistance, and Diversity of Campylobacter Isolated from U.S. Goat Feces: 2019 NAHMS Survey.

Goats are often asymptomatic carriers of Campylobacter, including the foodborne pathogen Campylobacter jejuni. Infections can have significant and economically detrimental health outcomes in both humans and animals. The primary objective of this study was to estimate the prevalence of Campylobacter in U.S. goat herds. Campylobacter species were isolated from 106 of 3,959 individual animals and from 42 of 277 goat operations that participated in fecal sample collection as part of the National Animal Health Monitoring System Goat 2019 study. Weighted animal-level prevalence was 2.3% (SE = 0.5%) and operation prevalence was 13.0% (SE = 3.2%). Animal-level prevalence ranged widely from 0 to 70.0%, however, 52.4% of positive operations (22/42) had only a single isolate. C. jejuni was the most frequently isolated species (68.9%; 73/106), followed by C. coli (29.3%, 31/106). A total of 46.2% (36/78) of viable isolates were pan-susceptible to 8 antimicrobials. Resistance to tetracycline (TET) was observed in 44.9% (35/78) of isolates, while 12.8% (10/78) were resistant to ciprofloxacin (CIP) and nalidixic acid (NAL). Among all isolates, a single resistance profile CIP-NAL-TET was observed in 3.8% (3/78) of isolates. A total of 35 unique sequence types (STs) were identified, 11 of which are potentially new. Multiple C. jejuni STs were observed in 48.1% (13/27) of positive operations. Goats with access to surface water, operations reporting antibiotics in the feed or water (excluding ionophores and coccidiostats), and operations reporting abortions and without postabortion management tasks had significantly greater odds of being Campylobacter positive. This snapshot of the U.S. goat population enriches the limited pool of knowledge on Campylobacter species presence in U.S. goats.

Carbon nanomaterials for sweat-based sensors: a review.

Sweat is easily accessible from the human skin's surface. It is secreted by the eccrine glands and contains a wealth of physiological information, including metabolites and electrolytes like glucose and Na ions. Sweat is a particularly useful biofluid because of its easy and non-invasive access, unlike other biofluids, like blood. On the other hand, nanomaterials have started to show promise operation as a competitive substitute for biosensors and molecular sensors throughout the last 10 years. Among the most synthetic nanomaterials that are studied, applied, and discussed, carbon nanomaterials are special. They are desirable candidates for sensor applications because of their many intrinsic electrical, magnetic, and optical characteristics; their chemical diversity and simplicity of manipulation; their biocompatibility; and their effectiveness as a chemically resistant platform. Carbon nanofibers (CNFs), carbon dots (CDs), carbon nanotubes (CNTs), and graphene have been intensively investigated as molecular sensors or as components that can be integrated into devices. In this review, we summarize recent advances in the use of carbon nanomaterials as sweat sensors and consider how they can be utilized to detect a diverse range of analytes in sweat, such as glucose, ions, lactate, cortisol, uric acid, and pH.

Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory.

Predictive maintenance holds a crucial role in various industries such as the automotive, aviation and factory automation industries when it comes to expensive engine upkeep. Predicting engine maintenance intervals is vital for devising effective business management strategies, enhancing occupational safety and optimising efficiency. To achieve predictive maintenance, engine sensor data are harnessed to assess the wear and tear of engines. In this research, a Long Short-Term Memory (LSTM) architecture was employed to forecast the remaining lifespan of aircraft engines. The LSTM model was evaluated using the NASA Turbofan Engine Corruption Simulation dataset and its performance was benchmarked against alternative methodologies. The results of these applications demonstrated exceptional outcomes, with the LSTM model achieving the highest classification accuracy at 98.916% and the lowest mean average absolute error at 1.284%.

Durability Assessment of Bonded Piezoelectric Wafer Active Sensors for Aircraft Health Monitoring Applications.

This study conducted experimental and numerical investigations on piezoelectric wafer active sensors (PWASs) bonded to an aluminum plate to assess the impact of bonding degradation on Lamb wave generation. Three surface-bonded PWASs were examined, including one intentionally bonded with a reduced adhesive to create a defective bond. Thermal cyclic aging was applied, monitoring through laser Doppler vibrometry (LDV) and static capacitance measurements. The PWAS with the initially defective bond exhibited the poorest performance over aging cycles, emphasizing the significance of the initial bond condition. As debonding progressed, modifications in electromechanical behavior were observed, leading to a reduction in wave amplitude and distortion of the generated wave field, challenging the validity of existing analytical modeling of wave-tuning curves for perfectly bonded PWASs. Both numerical simulations and experimental observations substantiated this finding. In conclusion, this study highlights the imperative of a high-integrity bond for the proper functioning of a guided wave-based structural health monitoring (SHM) system, emphasizing ongoing challenges in assessing SHM performance.

Development of Self-Sensing Asphalt Pavements: Review and Perspectives.

The digitalization of the road transport sector necessitates the exploration of new sensing technologies that are cost-effective, high-performing, and durable. Traditional sensing systems suffer from limitations, including incompatibility with asphalt mixtures and low durability. To address these challenges, the development of self-sensing asphalt pavements has emerged as a promising solution. These pavements are composed of stimuli-responsive materials capable of exhibiting changes in their electrical properties in response to external stimuli such as strain, damage, temperature, and humidity. Self-sensing asphalt pavements have numerous applications, including in relation to structural health monitoring (SHM), traffic monitoring, Digital Twins (DT), and Vehicle-to-Infrastructure Communication (V2I) tools. This paper serves as a foundation for the advancement of self-sensing asphalt pavements by providing a comprehensive review of the underlying principles, the composition of asphalt-based self-sensing materials, laboratory assessment techniques, and the full-scale implementation of this innovative technology.

Research on Magnetic Field-Based Damage Detection Technology for Ferromagnetic Microwires.

Composite materials are frequently exposed to external factors during their operational service, resulting in internal structural damage which subsequently impacts their structural performance. This paper employs ferromagnetic materials for their sensitivity to magnetic field strength. By detecting variations in the magnetic field within the embedded ferromagnetic microwires of composite materials, the aim is to indirectly assess the health status of the composite materials. Firstly, a theoretical numerical model for magnetic field intensity at the crack site was established. Subsequently, a finite element model was employed to analyze the variations in the magnetic characteristics of ferromagnetic microwires at the crack site. Under different parameter conditions, the patterns of magnetic signals at the crack site were determined. The results indicate that with an increase in the angle between the external magnetic field and the crack, the fitted curve of the magnetic signal shows a linear increase. The distance between the peak and valley of the radial magnetic signal in the axial direction decreases, and the axial magnetic signal transitions from double-peak to single-peak. With the increase in crack depth, the fitted curve of the magnetic signal shows a linear increase, and the magnetic signal at the crack tip also exhibits a linear increase. An increase in crack width leads to a non-linear decrease in the fitted curve of the magnetic signal, and after reaching a certain width, the magnetic signal stabilizes. For two identical cracks at different distances, the magnetic signal exhibits a transition from a complete pattern to two complete patterns. With the increase in the external magnetic field, the magnetic signal shows a completely regular linear increase. By analyzing and calculating the variations in magnetic signals, the patterns of magnetic characteristics under the damaged state of ferromagnetic microwires were obtained. This serves as a basis for assessing whether they can continue in service and for evaluating the overall health status of composite materials.

A Transferable Lidar-Based Method to Conduct Contactless Assessments of Gait Parameters in Diverse Home-like Environments.

Gait abnormalities in older adults are linked to increased risks of falls, institutionalization, and mortality, necessitating accurate and frequent gait assessments beyond traditional clinical settings. Current methods, such as pressure-sensitive walkways, often lack the continuous natural environment monitoring needed to understand an individual's gait fully during their daily activities. To address this gap, we present a Lidar-based method capable of unobtrusively and continuously tracking human leg movements in diverse home-like environments, aiming to match the accuracy of a clinical reference measurement system. We developed a calibration-free step extraction algorithm based on mathematical morphology to realize Lidar-based gait analysis. Clinical gait parameters of 45 healthy individuals were measured using Lidar and reference systems (a pressure-sensitive walkway and a video recording system). Each participant participated in three predefined ambulation experiments by walking over the walkway. We observed linear relationships with strong positive correlations (R2>0.9) between the values of the gait parameters (step and stride length, step and stride time, cadence, and velocity) measured with the Lidar sensors and the pressure-sensitive walkway reference system. Moreover, the lower and upper 95% confidence intervals of all gait parameters were tight. The proposed algorithm can accurately derive gait parameters from Lidar data captured in home-like environments, with a performance not significantly less accurate than clinical reference systems.

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers.

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.