Nowadays, more and more people are working remotely or in professions that require them to sit for long periods of time. Unfortunately, spending too much time in a seated position can lead to a range of physical and mental health problems, such as musculoskeletal discomfort, headaches, and respiratory issues. These problems are often exacerbated by poor posture, which is common when sitting for extended periods of time. To address this issue, we have developed a system for classifying sitting postures using sensors and machine learning algorithms, achieving 100% of accuracy with a set of seven fiber Bragg grating sensors. We have further optimized the multisensor system by studying the optimal number of sensors and their positioning on the spine, achieving over 95% accuracy in classifying upright, kyphotic, and lordotic positions with as little as only two devices.

Due to prolonged incorrect sitting posture, upper body musculoskeletal disorders (UBMDs) are largely widespread among sedentary workers. Monitoring employees’ sitting behaviors could be of great help in minimizing UBMDs’ occurrence. In addition, being primarily influenced by psycho-physical stress conditions, respiratory rate (RR) would be a further useful parameter to delineate the workers’ state of health. Wearable systems have emerged as a viable option for sitting posture and RR monitoring since enable continuous data collecting with no posture disturbances. Nevertheless, the main limits are poor fit, cumbersomeness, and movement restriction resulting in discomfort for the user. In addition, only few wearable solutions can track both these parameters contextually. To address these problems, in this study a flexible wearable system composed of seven modular sensing elements based on fiber Bragg grating (FBG) technology and designed to be worn on the back has been proposed to recognize the most common sitting postures (i.e., kyphotic, upright and lordotic) and estimate RR. The assessment was performed on ten volunteers showing good performances in postures recognition via Naïve Bayes classificator (accuracy >96.9%) and agreement with the benchmark in RR estimation (MAPE ranging between 0.74% and 3.83%, MODs close to zero, and LOAs between 0.76 bpm and 3.63 bpm). The method was then successfully tested on three additional subjects under different breathing conditions. The wearable system could offer great support for a better understanding of the workers’ posture attitudes and contribute to gathering RR information to depict an overall picture of the users’ state of health.

Respiratory diseases are common in post-stroke hemiplegic patients and represent a major social problem as they worsen the quality of life and reduce the life span. As a consequence, being able to monitor respiratory parameters such as the respiratory rate (RR) and assess the presence of respiratory asynchronies could be of paramount importance to define hemiplegics’ health status. Moreover, RR is a useful parameter to investigate the level of fatigue and distress that these patients undergo during rehabilitation processes. Although motion capture systems and flowmeters are the leading instruments for respiratory pattern evaluation, smart wearable systems are gaining ever more acceptance since they allow continuous monitoring by detecting chest wall breathing displacements, ensuring reduced costs and no need for dedicated spaces. Among other sensing technologies, fiber Bragg grating (FBG) sensors have emerged thanks to their high sensitivity to strain, lightness, and multiplexing capability. In this work, a wearable system composed of four flexible dumbbell-shaped sensing modules is proposed for respiratory monitoring in hemiplegic patients. The system is light and easy to wear and can be adapted to any anthropometry thanks to the modular anchoring system. Its feasibility assessment in RR evaluation was performed on seven hemiplegic volunteers in eupnea and tachypnea breathing conditions. In addition, an explorative investigation was conducted to assess the system’s ability to detect asynchronies between torso compartments. The good results suggest that this device could be a useful instrument to support clinicians and operators in hemiplegic patients’ management.

Quantitatively assessing personal health status is gaining increasing attention due to the improvement of diagnostic technology and the increasing occurrence of chronic pathologies. Monitoring physiological parameters allows for retrieving a general overview of the personal health status. Respiratory activity can provide relevant information, especially when pathologies affect the muscles and organs involved in breathing. Among many technologies, wearables may represent a valid solution for continuous and remote monitoring of respiratory activity, thus reducing healthcare costs. The most popular wearables used in this arena are based on detecting the breathing-induced movement of the chest wall. Therefore, their use in patients with impaired chest wall motion and abnormal respiratory kinematics can be challenging, but literature is still in its infancy. This study investigates the performance of a custom wearable device for respiratory monitoring in post-stroke patients. We tested the device on six hemiplegic patients under different respiratory regimes. The estimated respiratory parameters (i.e., respiratory frequency and the timing of the respiratory phase) demonstrated good agreement with the ones provided by a gold standard device. The promising results of this pilot study encourage the exploitation of wearables on these patients that may strongly impact the treatment of chronic diseases, such as hemiplegia.

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.

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. This study aims to investigate the influence of power delivered on radiofrequency catheter ablation (RFCA) effects by means of high resolved 2D temperature maps. 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. 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. 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.

His bundle pacing (HBP) has emerged as a feasible alternative to right (RVP) and biventricular pacing (BVP) for Cardiac Resynchronization Therapy (CRT). This study sought to assess, in ex-vivo experimental models, the optimal setup for HBP in terms of electrode placement and pacing protocol to achieve superior electrical synchrony in the case of complete His-Purkinje block and left bundle branch block (LBBB). We developed a 3D model of His bundle and bundle branches, embedded in a patient-specific biventricular heart model reconstructed from CT images. A monodomain reaction-diffusion model was adopted to describe the propagation of cardiac action potential, and a custom procedure was developed to compute pseudo-ECGs. Experimental measurements of tip electrode potential waveforms have been performed on ex-vivo swine myocardium to determine the appropriate boundary condition for delivering the electrical stimulus in the numerical model. An extended parametric analysis, investigating the effect of the electrode orientation and helix length, pacing protocol, and atrioventricular delay, allowed us to determine the optimal setup for HBP therapy. Both selective (S-HBP) and non-selective (NS-HBP) His bundle pacing were tested, as the variable anatomical location of the His bundle can result in the activation of the surrounding myocardium. Our study indicates a perpendicular placement of the electrode as the most advantageous for restoring the physiological function of the His-Purkinje system. We found that higher-energy protocols can compensate for the effects of an angled placement though concurring to potential tip fibrosis. Promisingly, we also revealed that an increased electrode helix length can provide optimal resynchronization even with low-energy pacing protocols. Our results provide informative guidance for implant procedure and therapy optimization, which will hopefully have clinical implications further improving the procedural success rates and patients' quality of life, due to reduced incidence of lead revision and onset of complications.

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.

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.

Atrial fibrillation (AF) is the most recurrent type of cardiac arrhythmia that causes a major socio-economic burden as associated with significant morbidity and mortality. Radiofrequency catheter ablation (RFCA) is a leading clinical practice for the treatment of AF. The aim of the procedure is to induce necrosis in the ectopic foci responsible for the altered electrical pathway through temperature increments provoked by radiofrequency delivery. In this context, temperature is a key factor as determines the size of the produced thermal lesions and, in turn, the success or the failure of the treatment. As consequence, several methods have been exploited for RFCA temperature monitoring, but with several limitations. In recent times, the feasibility of using fiber Bragg grating (FBG) sensors for high-resolved and distributed temperature measurements in ex vivo myocardial swine tissues has been assessed. In this study, the heat diffusion within the tissues was investigated by producing 2D thermal maps based on multipoint FBG temperature data. Furthermore, the influence of both the delivered power and the treatment time on the dimensions of the produced thermal lesion was explored. The present research may lay the basis for the development of a model describing the spatio-temporal dynamics of the lesion formation. Such model may offer support to clinicians in selecting the proper RFCA settings.

Collaborative robots are expected to physically interact with humans in daily living and workplace, including industrial and healthcare settings. A related key enabling technology is tactile sensing, which currently requires addressing the outstanding scientific challenge to simultaneously detect contact location and intensity by means of soft conformable artificial skins adapting over large areas to the complex curved geometries of robot embodiments. In this work, the development of a large-area sensitive soft skin with a curved geometry is presented, allowing for robot total-body coverage through modular patches. The biomimetic skin consists of a soft polymeric matrix, resembling a human forearm, embedded with photonic Fiber Bragg Grating (FBG) transducers, which partially mimics Ruffini mechanoreceptor functionality with diffuse, overlapping receptive fields. A Convolutional Neural Network deep learning algorithm and a multigrid Neuron Integration Process were implemented to decode the FBG sensor outputs for inferring contact force magnitude and localization through the skin surface. Results achieved 35 mN (IQR = 56 mN) and 3.2 mm (IQR = 2.3 mm) median errors, for force and localization predictions, respectively. Demonstrations with an anthropomorphic arm pave the way towards AI-based integrated skins enabling safe human-robot cooperation via machine intelligence.

Systems for cardiorespiratory monitoring hold tremendous promise for many applications in clinical settings, harsh environments, sports science and telemonitoring. However, the translation in the real world is challenging because it is difficult to develop unobtrusive and comfortable systems with high metrological performance. Recent advances in fiber optic technology in the field of sensing and their application to instrument wearable systems can tackle the main issues in this field. In this work, we focused on the recent advances in cardiorespiratory monitoring of fiber Bragg grating (FBGs) sensors which are one of the most popular sensors based on fiber optic technology. We provide an overview of the FBGs working principle and performance. We also describe the most popular and promising configurations based on FBGs used for cardiorespiratory monitoring.

Workers experiencing high levels of stress may suffer from poor motivation, low level of attention, and physical disorders which, in turn, may lead to work injuries. Respiratory frequency (f(R)) is considered one of the most reliable indicators of the mental load and fatigue state of workers. Monitoring this parameter through wearable devices represents an efficient solution for the maintenance of Occupational Health and Safety. We developed an innovative wearable system equipped with flexible sensors based on fiber Bragg gratings (FBGs) to monitor f(R) in static and dynamic conditions characterizing occupational settings. This article provides a description of the system and of the metrological characteristics of the flexible sensors in terms of response to strain and temperature changes, and hysteresis error. The performances of the device, evaluated in a laboratory during the execution of tasks mimicking real work activity, are also reported. The obtained promising results encourage the engineering of the system for use in real workplaces to collect quantitative information on the psychophysical state of workers and its relation to stress level.

The use of face masks as respiratory protective equipment (RPE) is considered key to maintaining the quality of life during emergency situations, long-term care and working conditions. Face masks can reduce the virus spread and bacterial infections as well as prevent the inhalation of industrial waste gases. Individuals who wear a facial mask over a prolonged time often reported uncomfortable feelings due to breathing resistance, heat, tightness, and overall discomfort. One of the main indicators used to quantify the level of discomfort induced by RPE is the respiratory rate (RR). In fact, RR can be directly associated with RPE-related unease since the presence of facial masks might intuitively modify the breathing pattern of the users. Unfortunately, still little is known about RR and its variability in response to wearing RPE. In the last year, the massive use of face masks due to COVID-19 pandemic fosters the development of sensors to measure RR once mounted into the medical mask. Among other, fiber Bragg grating sensors (FBGs) have gained growing attention since the intrinsic advantages of small size, lightweight, high metrological properties, and safety. In the present study, a single-use FFP2 surgical mask was instrumented by a soft sensor based on FBG to perform a longterm acquisition (i.e., 20 min) of the respiratory signal during ordinary work activities at the video terminal. The promising results confirmed the high accuracy of the proposed system in the estimation of RR with a maximum discrepancy of -0.69 breaths per minute and mean absolute percentage error of 2.88% when compared to a reference instrument. Moreover, no saturation of the sensor output occurred during the usage time.

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.

In this study, we assess the feasibility of highly dense fiber Bragg grating (FBG) arrays for real-time temperature measurement during Nanocomposites (NCs)-enhanced laser ablation (LA) of pancreas tissue. FBG arrays were fabricated with the femtosecond point-by-point writing technology. Each highly dense array contains 25 FBGs with a grating length of 0.9 mm and an edge-to-edge distance of 0.1 mm. As alternative fiber sensors, we used commercially available acrylatecoated FBG arrays containing 5 FBGs. Temperature measurements by the highly dense FBG array were compared to thermal camera readings during laser irradiation of water samples. The augmented thermal effect produced by special NC comprising of a polydopamine matrix embedded with gold and copper was evaluated during the irradiation of an ex vivo phantom. The phantom consisted of a blended porcine pancreas tissue mixed with the NC; tissue mixed with water was used for control. The results clearly demonstrate that the highly dense arrays better detect the peak temperature and temperature distribution. The NC presence increased the maximum temperature reached during LA from 48°C (control) to 90°C (NC) at 2 mm, and from 33 °C to 36°C at 4 mm distance from the laser tip. The low spatial resolution of the commercial arrays produced an underestimation of the peak temperature by 2°C (control), and by 1°C (NC) at 4 mm. These results highlight the importance of the proper selection of the measurement system characteristics, especially when high temperature gradient should be measured in biological tissues undergoing thermal ablation for cancer treatment.

Recognition of occupational stressors is a goal of primary importance to improve the psycho-physical health of workers. Many studies agree in considering the respiratory rate (RR) as a key parameter to be monitored since changes from normal physiological ranges can be closely related to states of mental and physical stress. Despite the wide availability of solutions devoted to RR detection, the utilization of light and unobtrusive wearable systems exploiting several sensing technologies is becoming more and more popular in recent years. Among others, fiber Bragg gratings (FBGs) are gaining momentum due to their valuable properties (e.g., lightness, multiplexing capability, high sensitivity etc.) which make them suitable to be integrated into garments and appropriate for RR monitoring purpose. In this work, the feasibility assessment of an FBG-based wearable system for RR detection in a static occupational scenario is presented. A chest strap instrumented with two flexible sensors based on an FBG technology was exploited to perform 40 min of RR tracking on two video terminal workers. The proposed system was able to measure the respiratory signal over a prolonged time and showed high capability to estimate RR when compared to a reference instrument, as evinced by the obtained values of mean absolute error (MAE), always ≤ 1 bpm.

Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively.

There is a growing demand of comfortable and unobtrusive wearable systems for monitoring a variety of physiological parameters and in particular the respiratory frequency ( ${f}_{R}$ ). The most popular techniques for ${f}_{R}$ monitoring cannot be used in several clinical applications and in unstructured environment. These issues have fostered a dramatic growth of interest for wearable systems devoted to monitor ${f}_{R}$ . In this arena, fiber Bragg grating (FBG) sensors have gained due to a variety of benefits. In this work, we designed and fabricated an FBG-based wearable device for ${f}_{R}$ monitoring from the nasal airflow. The proposed design does not require a mask to improve the comfortability and acceptability of the system. The sensing element was functionalized by a hygroscopic coating material to make the FBG sensitive to relative humidity changes. This feature allows calculating ${f}_{R}$ starting from the discrimination between the inspiration and expiration phases. A pilot study on 6 volunteers was designed to assess the system during three different breathing stages (i.e., slow, normal and fast breathing). Results showed high performance of the proposed wearable device in detecting mean and breath-by-breath ${f}_{R}$ values (i.e., mean percentage errors ≤ 2.29 % and bias ≤ 0.31 breaths per minute) during slow breathing, normal breathing, and fast breathing.

Many minimally invasive thermal techniques, such as radiofrequency ablation, laser ablation, and microwave ablation (MWA) have gained substantial attention and broad clinical acceptance for the treatment of several tumors. All these treatments cause a high temperature increment, cytotoxic for the cells within the target organ. The knowledge of the ablation process effects in terms of temperature increment can be crucial to optimize the treatment settings and to avoid the occurrence of damages the healthy structures located around the tumor. During the last years, several feasibility studies on the use of MWA for bone tumor have been carried out, but an experimental analysis of the temperature reached within the bone during the treatment has not been performed. The aim of this work is to present a multi-point temperature measurement reached within the bone during MWA. Experiments were performed on an ex vivo bovine femur using a MWA system at a power of 75 W and a treatment time of 8 min. Femur temperature was measured in 30 points inside the bone and around the MW antenna, covering a large area. The measurements were performed by using three fiber optics embedding 10 fiber Bragg grating sensors each. Results of this pilot study showed the capability of the proposed approach for a multi-point temperature monitoring in bone undergoing MWA. Together, these experiments represent the first step towards the design of a system capable of understanding the effects of MWA on bone tumors, to rationally define the best MWA settings for the treatment of a specific tumor, and so to improve the treatment outcomes in this promising clinical application.