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Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMSs) perform real-time monitoring functions, but their performance must be carefully assessed. This is typically done by introducing artificial damages to the components; however, such a procedure requires the production and testing of a large number of structural elements. In this work, the damage detection performance of a strain-based SHMS was evaluated on a composite helicopter rotor blade root, exploiting a Finite Element (FE) model of the component. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through FE analyses. The effects of the load's variability and of the damage were decoupled by including a load recognition step in the algorithm, which was accomplished either with an Artificial Neural Network (ANN) or a calibration matrix. Anomaly detection, damage assessment, and localization were performed by using an ANN. The results showed a higher load identification and anomaly detection accuracy using an ANN for the load recognition, and the load set was recognized with a satisfactory accuracy, even in damaged blades. This case study was focused on a real-world subcomponent with complex geometrical features and realistic load conditions, which was not investigated in the literature and provided a promising approach to estimate the performance of a strain-based SHMS.

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To investigate the effects of long-term prestress loss on concrete box girders strengthened with external prestressing, a large-span box girder, in service for over 20 years and strengthened with external prestressing, was monitored for four months. Prestress loss in the longitudinal, vertical, and transverse directions of the box girder was calculated according to Chinese code requirements. Magnetic flux rope force transducers were used to monitor the prestress loss in the external prestressing cables. Fiber Bragg Grating (FBG) sensors were used to monitor deflection changes at the mid-span of the bridge. Finally, the effect of prestress loss in the longitudinal, vertical, and transverse tendons on mid-span deflection was investigated through simulations using ABAQUS software. The results show that instantaneous prestress loss accounts for most of the total loss compared to long-term loss, and that longitudinal prestress loss has the most significant effect on mid-span deflection. The impact of longitudinal prestress loss on deflection before and after strengthening was also compared. The downward deflection and up-ward arch caused by longitudinal tendon prestress loss were reduced after strengthening, con-firming the effectiveness of the external prestressing method.

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Batteries play a crucial role as energy storage devices across various industries. However, achieving high performance often comes at the cost of safety. Continuous monitoring is essential to ensure the safety and reliability of batteries. This paper investigates the advancements in battery monitoring technology, focusing on fiber Bragg gratings (FBGs). By examining the factors contributing to battery degradation and the principles of FBGs, this study discusses key aspects of FBG sensing, including mounting locations, monitoring targets, and their correlation with optical signals. While current FBG battery sensing can achieve high measurement accuracies for temperature (0.1 °C), strain (0.1 µÎµ), pressure (0.14 bar), and refractive index (6 × 10-5 RIU), with corresponding sensitivities of 40 pm/°C, 2.2 pm/µÎµ, -0.3 pm/bar, and -18 nm/RIU, respectively, accurately assessing battery health in real time remains a challenge. Traditional methods struggle to provide real-time and precise evaluations by analyzing the microstructure of battery materials or physical phenomena during chemical reactions. Therefore, by summarizing the current state of FBG battery sensing research, it is evident that monitoring battery material properties (e.g., refractive index and gas properties) through FBGs offers a promising solution for real-time and accurate battery health assessment. This paper also delves into the obstacles of battery monitoring, such as standardizing the FBG encapsulation process, decoupling multiple parameters, and controlling costs. Ultimately, the paper highlights the potential of FBG monitoring technology in driving advancements in battery development.

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In this article, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots based on proprioceptive sensing feedback. Developments of 3-D shape perception and control technologies are crucial for continuum and soft robots to perform tasks autonomously in surgical interventions. However, owing to the nonlinear properties of continuum robots, one main difficulty lies in the modeling of them, especially for soft robots with variable stiffness. To address this problem, we propose a versatile learning-based adaptive shape controller by leveraging proprioception of 3-D configuration from fiber Bragg grating (FBG) sensors, which can online estimate the unknown model of continuum robot against unexpected disturbances and exhibit an adaptive behavior to the unmodeled system without priori data exploration. Based on a new composite adaptation algorithm, the asymptotic convergences of the closed-loop system with learning parameters have been proven by Lyapunov theory. To validate the proposed method, we present a comprehensive experimental study using two continuum and soft robots both integrated with multicore FBGs, including a robotic-assisted colonoscope and multisection extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments, as well as phantom experiments.

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Accurately mapping the temperature during ablation is crucial for improving clinical outcomes. While various sensor configurations have been suggested in the literature, depending on the sensors' type, number, and size, a comprehensive understanding of optimizing these parameters for precise temperature reconstruction is still lacking. This study addresses this gap by introducing a tool based on a theoretical model to optimize the placement of fiber Bragg grating sensors (FBG) within the organ undergoing ablation. The theoretical model serves as a general framework, allowing for adaptation to various situations. In practical application, the model provides a foundational structure, with the flexibility to tailor specific optimal solutions by adjusting problem-specific data. We propose a nonlinear and nonconvex (and, thus, only solvable in an approximated manner) optimization formulation to determine the optimal distribution and three-dimensional placement of FBG arrays. The optimization aims to find a trade-off among two objectives: maximizing the variance of the expected temperatures measured by the sensors, which can be obtained from a predictive simulation that considers both the type of applicator used and the specific organ involved, and maximizing the squared sum of the distances between the sensor pairs. The proposed approach provides a trade-off between collecting diverse temperatures and not having all the sensors concentrated in a single area. We address the optimization problem through the utilization of approximation schemes in programming. We then substantiate the efficacy of this approach through simulations. This study tackles optimizing the FBGs' sensor placement for precise temperature monitoring during tumor ablation. Optimizing the FBG placement enhances temperature mapping, aiding in tumor cell eradication while minimizing damage to surrounding tissues.

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PURPOSE: Radiofrequency ablation is an increasingly used surgical option for ablation, resection and coagulation of soft tissues in joint arthroscopy. One of the major issues of thermal ablation is the temperature monitoring across the target areas, as cellular mortality is a direct consequence of thermal dosimetry. Temperatures from 45 °C to 50 °C are at risk of damage to chondrocytes. One of the most reliable tools for temperature monitoring is represented by fiber optic sensors, as they allow accurate and real-time temperature measurement via a minimally invasive approach. The aim of this study was to determine, by fiber Bragg grating sensors (FBGs), the safety of radiofrequency ablation in tissue heating applied to ex-vivo bovine hip joints. METHODS: Ex vivo bovine hips were subjected to radiofrequency ablation, specifically in the acetabular labrum, for a total of two experiments. The WEREWOLF System (Smith + Nephew, Watford, UK) was employed in high operating mode and in a controlled ablation way. One optical fiber embedding seven FBGs was used to record multipoint temperature variations. Each sensor was 1 mm in length with a distance from edge to edge with each other of 2 mm. RESULTS: The maximum variation was recorded in both the tests by the FBG1 (i.e., the closest one to the electrode tip) and was lower than to 2.8 °C. The other sensors (from FBG2 to FBG7) did not record a significant temperature change throughout the duration of the experiment (maximum up to 0.7 °C for FBG7). CONCLUSIONS: No significant increase in temperature was observed at any of the seven sites. The sensor nearest to the radiofrequency source exhibited the highest temperature rise, but the variation was only 3 °C. The minimal temperature increase registered at the measurement sites, according to existing literature, is not expected to be cytotoxic. FBGs demonstrate the potential to fulfil the strict requirements for temperature measurements during arthroscopic surgery.

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Lithium-ion batteries are widely used in a variety of fields due to their high energy density, high power density, long service life, and environmental friendliness. However, safety accidents with lithium-ion batteries occur frequently. The real-time safety monitoring of lithium-ion batteries is particularly important during their use. The fiber Bragg grating (FBG) sensors have some additional advantages over conventional electrochemical sensors, such as low invasiveness, electromagnetic anti-interference, and insulating properties. This paper reviews lithium-ion battery safety monitoring based on FBG sensors. The principles and sensing performance of FBG sensors are described. The single-parameter monitoring and dual-parameter monitoring of lithium-ion batteries based on FBG sensors are reviewed. The current application state of the monitored data in lithium-ion batteries is summarized. We also present a brief overview of the recent developments in FBG sensors used in lithium-ion batteries. Finally, we discuss future trends in lithium-ion battery safety monitoring based on FBG sensors.

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This paper demonstrates that a graded-index (GRIN) phononic lens, combined with a channel waveguide, can focus anti-symmetric Lamb waves for extraction by a detector with strong directional sensitivity. Guided ultrasonic wave inspection is commonly applied for structural health monitoring applications; however, obtaining sufficient signal amplitude is a challenge. In addition, fiber Bragg grating (FBG) sensors have strong directional sensitivity. We fabricate the GRIN structure, followed by a channel waveguide starting at the focal point, using a commercial 3D printer and mount it on a thin aluminum plate. We characterize the focusing of the A0 mode Lamb wave in the plate, traveling across the GRIN lens using 3D laser Doppler vibrometry. We also measure the extraction of focused energy using an FBG sensor, examining the optimal sensor bond location and bond length in the channel of the waveguide for maximum signal extraction. The measured amplification of the ultrasound signal is compared to theoretical predictions. The results demonstrate that significant amplification of the waveform is achieved and that selecting the location of the FBG sensor in the channel is critical to optimizing the amplification.

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Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface by the mechanical activity of the heart are becoming popular. For what concerns wearable-based methods, cardiac vibrations can be recorded from the thorax in the form of acceleration, angular velocity, and/or displacement by means of accelerometers, gyroscopes, and fiber optic sensors, respectively. The present paper reviews the currently available wearables for measuring precordial vibrations. The focus is on sensor technology and signal processing techniques for the extraction of the parameters of interest. Lastly, the explored application scenarios and experimental protocols with the relative influencing factors are discussed for each technique. The goal is to delve into these three fundamental aspects (i.e., wearable system, signal processing, and application scenario), which are mutually interrelated, to give a holistic view of the whole process, beyond the sensor aspect alone. The reader can gain a more complete picture of this context without disregarding any of these 3 aspects.

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The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03 L/min, when the minimum number of sensors was considered, to 9.97 L/min for the complete setting (target flow range: 69.231 ± 21.477 L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r = 0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE = 10.99 L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications.

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Dual-comb multiheterodyne spectroscopy is a well-established technology for the highly sensitive real-time detection and measurement of the optical spectra of samples, including gases and fiber sensors. However, a common drawback of dual-comb spectroscopy is the need for a broadband amplitude-resolved absorption or reflection measurement, which increases the complexity of the dual comb and requires the precise calibration of the optical detection. In the present study, we present an alternative dispersion-based approach applied to fiber Bragg grating sensors in which the dual comb is compacted by a single dual-drive-unit optical modulator, and the fiber sensor is part of a dispersion interferometer. The incident dual comb samples a few points in the spectrum that are sensitive to Bragg wavelength changes through the optical phase. The spectra reading is improved due to the external interferometer and is desensitized to changes in the amplitude of the comb tones. The narrow-band detection of the fiber sensor dispersion changes that we demonstrate enables the compact, cost-effective, high-resolution multiheterodyne interrogation of high-throughput interferometric fiber sensors. These characteristics open its application both to the detection of fast phenomena, such as ultrasound, and to the precise measurement at high speed of chemical-/biological-sensing samples. The results with a low-reflectivity fiber Bragg grating show the detection of dynamic strain in the range of 215 nε with a 30 dB signal to noise ratio and up to 130 kHz (ultrasonic range).

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Radiofrequency catheter ablation (RFCA) is the mainstream treatment for drug-refractory cardiac fibrillation. Multiple studies demonstrated that incorrect dosage of radiofrequency energy to the myocardium could lead to uncontrolled tissue damage or treatment failure, with the consequent need for unplanned reoperations. Monitoring tissue temperature during thermal therapy and predicting the extent of lesions may improve treatment efficacy. Cardiac computational modeling represents a viable tool for identifying optimal RFCA settings, though predictability issues still limit a widespread usage of such a technology in clinical scenarios. We aim to fill this gap by assessing the influence of the intrinsic myocardial microstructure on the thermo-electric behavior at the tissue level. By performing multi-point temperature measurements on ex-vivo swine cardiac tissue samples, the experimental characterization of myocardial thermal anisotropy allowed us to assemble a fine-tuned thermo-electric material model of the cardiac tissue. We implemented a multiphysics and multiscale computational framework, encompassing thermo-electric anisotropic conduction, phase-lagging for heat transfer, and a three-state dynamical system for cellular death and lesion estimation. Our analysis resulted in a remarkable agreement between ex-vivo measurements and numerical results. Accordingly, we identified myocardium anisotropy as the driving effect on the outcomes of hyperthermic treatments. Furthermore, we characterized the complex nonlinear couplings regulating tissue behavior during RFCA, discussing model calibration, limitations, and perspectives.

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BACKGROUND: Radiofrequency (RF) lesion creation is related to the heat propagation induced by RF application on tissues. Thermocouple embedded in the RF antenna are not able to predict deep tissue temperature at various level. OBJECTIVES: This study aims to investigate the influence of power delivered on radiofrequency catheter ablation (RFCA) effects by means of high resolved 2D temperature maps. METHODS: Three trials of four ablations (12 applications) were executed on each specimen of healthy excised swine myocardium in different application points at four RF power values (30 W, 40 W, 50 W, and 60 W) for a fixed treatment time. All the data provided by the fiber Bragg gratings (FBGs) were analyzed. Temperature variations (ΔT) in time recorded in the 28 sites of measurements were reported. Also, temperature maps showing the ΔT spatial distribution reached within the tissue at the end of the RFCA were produced and displayed, together with the representation of the lethal isotherm. Moreover, the time of achievement of the lethal isotherm at different tissue depths (from 1 to 8 mm) was evaluated for the four power settings. RESULTS: Temperature trends reported comparable profiles across the different power settings. ΔT values and ΔT rising times showed dependence on the sensors' proximity to the RF energy source and on the set RF power. Temperature maps confirmed that heat propagation occurs preferentially along the width of the tissue than in the depth. Also, for the adjusted treatment time, no power setting guarantees lesions thicker than 6 mm. CONCLUSIONS: ΔT maximal values and ΔT rising time strongly depends on the proximity of the tissues to RF energy source, as well as on the RF power setting. A plateau is reached in lesion size, regardless of the power setting. A first correlation between lesion size, power setting, and time to achieve lethal isotherms has been established.

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Coral sand cement (CSC) mortar is increasingly used in reef projects, which is prepared by mixing coral sand with cement and water in certain proportions. Considering that early-age hydration behavior is closely related to the strength and durability of the mortar, the early-age hydration process and micro-morphology of CSC mortars with various water-cement ratios (W/C) and sand-cement ratios (S/C) were studied. A monitoring system based on FBG is proposed in this paper, which uses the high sensitivity and conformability of optical fiber to measure the hydration temperature and internal shrinkage strain simultaneously and continuously. The standard sand cement (SSC) mortar with the same sand gradation and mix proportion is also prepared for comparison. The micro-morphology is observed by a scanning electron microscope (SEM) for measurement results' explanation. The results show that the variation of the hydration temperature and shrinkage strain with hydration time of both CSC mortars and SSC mortars follow a unimodal function. Differently, the peak hydration temperature for CSC is obviously lower than that of SSC. The peak temperature of CSC mortar decreases linearly with the increase in S/C, and the decrease rate of the peak temperature is higher for CSC with small W/C than that with higher W/C. For mortars with lower W/C, the peak shrinkage strain of CSC is larger than that of SSC. Meanwhile, for mortars with higher W/C, the peak shrinkage strain of CSC changes to be lower than that of SSC, which is attributed to the significant water absorption characteristic of CSC. Therefore, as an eco-friendly lightweight aggregate, CS is more suitable than SS for the design of high W/C and alleviating the hydration heat of mass concrete under the meeting of strength.

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In this study, a novel wearable system for the identification of compensatory trunk movements (CTMs) in post-stroke hemiplegic patients is presented. The device is composed of seven soft sensing elements (SSEs) based on fiber Bragg grating (FBG) technology. Each SSE consists of a single FBG encapsulated into a flexible matrix to enhance the sensor's robustness and improve its compliance with the human body. The FBG's small size, light weight, multiplexing capability, and biocompatibility make the proposed wearable system suitable for multi-point measurements without any movement restriction. Firstly, its manufacturing process is presented, together with the SSEs' mechanical characterization to strain. Results of the metrological characterization showed a linear response of each SSE in the operating range. Then, the feasibility assessment of the proposed system is described. In particular, the device's capability of detecting CTMs was assessed on 10 healthy volunteers and eight hemiplegic patients while performing three tasks which are representative of typical everyday life actions. The wearable system showed good potential in detecting CTMs. This promising result may foster the use of the proposed device on post-stroke patients, aiming at assessing the proper course of the rehabilitation process both in clinical and domestic settings. Moreover, its use may aid in defining tailored strategies to improve post-stoke patients' motor recovery and quality of life.

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Plants are primary resources for oxygen and foods whose production is fundamental for our life. However, diseases and pests may interfere with plant growth and cause a significant reduction of both the quality and quantity of agriculture products. Increasing agricultural productivity is crucial for poverty reduction and food security improvements. For this reason, the 2030 Agenda for Sustainable Development gives a central role to agriculture by promoting a strong technological innovation for advancing sustainable practices at the plant level. To accomplish this aim, recently, wearable sensors and flexible electronics have been extended from humans to plants for measuring elongation, microclimate, and stressing factors that may affect the plant's healthy growth. Unexpectedly, fiber Bragg gratings (FBGs), which are very popular in health monitoring applications ranging from civil infrastructures to the human body, are still overlooked for the agriculture sector. In this work, for the first time, plant wearables based on FBG technology are proposed for the continuous and simultaneous monitoring of plant growth and environmental parameters (i.e., temperature and humidity) in real settings. The promising results demonstrated the feasibility of FBG-based sensors to work in real situations by holding the promise to advance continuous and accurate plant health growth monitoring techniques.

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Bone metastases and osteoid osteoma (OO) have a high incidence in patients facing primary lesions in many organs. Radiotherapy has long been the standard choice for these patients, performed as stand-alone or in conjunction with surgery. However, the needs of these patients have never been fully met, especially in the ones with low life expectancy, where treatments devoted to pain reduction are pivotal. New techniques as hyperthermia treatments (HTs) are emerging to reduce the associated pain of bone metastases and OO. Temperature monitoring during HTs may significantly improve the clinical outcomes since the amount of thermal injury depends on the tissue temperature and the exposure time. This is particularly relevant in bone tumors due to the adjacent vulnerable structures (e.g., spinal cord and nerve roots). In this Review, we focus on the potential of temperature monitoring on HT of bone cancer. Preclinical and clinical studies have been proposed and are underway to investigate the use of different thermometric techniques in this scenario. We review these studies, the principle of work of the thermometric techniques used in HTs, their strengths, weaknesses, and pitfalls, as well as the strategies and the potential of improving the HTs outcomes.

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Cardiac radiofrequency ablation (RFA) has received substantial attention for the treatment of multiple arrhythmias. In this scenario, there is an ever-growing demand for monitoring the temperature trend inside the tissue as it may allow an accurate control of the treatment effects, with a consequent improvement of the clinical outcomes. There are many methods for monitoring temperature in tissues undergoing RFA, which can be divided into invasive and non-invasive. This paper aims to provide an overview of the currently available techniques for temperature detection in this clinical scenario. Firstly, we describe the heat generation during RFA, then we report the principle of work of the most popular thermometric techniques and their features. Finally, we introduce their main applications in the field of cardiac RFA to explore the applicability in clinical settings of each method.

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In this paper, we evaluate the application of curved detectors and freeform optics technologies for fiber Bragg gratings (FBGs) interrogation monitors design. It is shown that, in a high-dispersion spectrograph scheme, the camera part operates in special conditions, which result in a field curvature change. This field curvature can be compensated by the use of a curved detector. When used together with freeform optics, the curved detectors allow for reduction of the number of optical components to two or even one element by merging their functions. Three design examples for the range of 810-860 nm reaching the spectral resolution limit of 89-139 pm at NA=0.14 are presented to demonstrate the achieved performance and the technological trade-offs.

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Radiofrequency ablation (RFA) is the most widely used technique for the treatment of cardiac arrhythmias. A variety of factors, such as the electrode tip shape, the force exerted on the tissue by the catheter and the delivered power, combine to determine the temperature distribution, and as consequence, the lesion shape and size. In this context, being able to know the temperature reached in the myocardium during the RFA can be helpful for predicting the lesion dimensions to prevent the occurrence of undesired tissue damage. The catheters used so far in such procedures provide single-point temperature measurements within the probe (by means of embedded thermocouples or thermistors), so no information regarding the temperature changes occurring in myocardial tissues can be retrieved. The aim of this study was to assess the feasibility of fiber Bragg grating sensors (FBGs) to perform multi-point and millimetric-scale temperature measurements within myocardium subjected to RFA. The assessment has been performed on ex vivo porcine myocardium specimens undergoing RFA. Data show the feasibility of the proposed solution in providing spatial temperature distribution within the myocardial tissue during the entire RFA. These high-resolved measurements may allow reconstructing the temperature distribution in the tissue. This study lays the foundations for the implementation of 3D thermal maps to investigate how the supplied power, treatment time, force of contact and irrigation flow of the catheter influence the thermal effects within the tissue.

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