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Understanding the differences in ventilatory responses during exercise between patients with fibromyalgia and those with other chronic pain disorders is crucial for developing effective therapeutic interventions, especially in exercise to identify the better physical therapy prescription. Both populations face unique challenges that impact their ability to engage in physical activity; yet, the underlying physiological responses can vary significantly. In this context, the methodology of this study entailed conducting a comparative analysis of the ventilatory response during exercise in patients with fibromyalgia and those with other chronic pain disorders. The experimental protocol included a total of 31 participants (n = 13 diagnosed with fibromyalgia and n = 18 diagnosed with other chronic pain conditions). All participants completed a stress test, where the ventilatory parameters were measured in three stages (i.e., resting, incremental exercise, and recovery). The results revealed significant differences (p<0.05) in ventilatory responses between both groups. Patients with fibromyalgia exhibited reduced time for the aerobic threshold and a higher respiratory frequency in the anaerobic threshold compared to those with other chronic pain disorders. Furthermore, fibromyalgia patients demonstrated higher values in the ventilatory coefficient during the test and in the recovery stage. In conclusion, these differences underscore the need for tailored exercise programs that specifically address the unique ventilatory challenges faced by fibromyalgia patients to improve their physical function and overall quality of life.

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Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.

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BACKGROUND: In the last ten years, the design and implementation of Optical Fiber Sensors (OFS) in biomedical applications have been discussed, with a focus on different subareas, such as body parameter monitoring and control of assistive devices. MATERIALS AND METHODS: A scoping review was performed including scientific literature (PubMed/Scopus, IEEE and Web of Science), patents (WIPO/Google Scholar), and commercial information. RESULTS: The main applications of OFS in the rehabilitation field for preventing future postural diseases and applying them in device controllers were discussed in this review. Physical characteristics of OFS, different uses, and applications of Polymer Optical Fiber pressure sensors are mentioned. The main postures used for posture monitoring analysis when the user is sitting are normal position, crooked back, high lumbar pressure, sitting on the edge of the chair, and crooked back, left position, and right position. Additionally, it is possible to use Machine Learning (ML) algorithms for posture classification, and device control such as Support Vector Machine, k-Nearest Neighbors, etc., obtaining accuracies above 97%. Moreover, the literature mentions wheelchair controllers and Graphical User Interfaces using pressure maps to provide feedback to the user. CONCLUSIONS: OFS have been used in several healthcare applications as well as postural and preventive applications. The literature showed an effort to implement and design accessible devices for people with disabilities and people with specific diseases. Alternatively, ML algorithms are widely used in this direction, leaving the door open for further studies that allow the application of real-time systems for posture monitoring and wheelchairs control.

IMPLICATIONS FOR REHABILITATIONPosture monitoring and ulcer detection systems are very useful to prevent or treat diseases related to bedsores or pressure ulcers using a different kind of electronic or optic instrumentation to improve the user's quality of life.The system characteristics using optical fiber sensors discussed in this review set an important precedent in the fabrication of low-cost systems for biomedical applications.

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Introduction: The rise of soft robotics has driven the development of devices for assistance in activities of daily living (ADL). Likewise, different types of actuation have been developed for safer human interaction. Recently, textile-based pneumatic actuation has been introduced in hand exoskeletons for features such as biocompatibility, flexibility, and durability. These devices have demonstrated their potential use in assisting ADLs, such as the degrees of freedom assisted, the force exerted, or the inclusion of sensors. However, performing ADLs requires the use of different objects, so exoskeletons must provide the ability to grasp and maintain stable contact with a variety of objects to lead to the successful development of ADLs. Although textile-based exoskeletons have demonstrated significant advancements, the ability of these devices to maintain stable contact with a variety of objects commonly used in ADLs has yet to be fully evaluated. Materials and methods: This paper presents the development and experimental validation in healthy users of a fabric-based soft hand exoskeleton through a grasping performance test using The Anthropomorphic Hand Assessment Protocol (AHAP), which assesses eight types of grasping with 24 objects of different shapes, sizes, textures, weights, and rigidities, and two standardized tests used in the rehabilitation processes of post- stroke patients. Results and discussion: A total of 10 healthy users (45.50 ± 14.93 years old) participated in this study. The results indicate that the device can assist in developing ADLs by evaluating the eight types of grasps of the AHAP. A score of 95.76 ± 2.90% out of 100% was obtained for the Maintaining Score, indicating that the ExHand Exoskeleton can maintain stable contact with various daily living objects. In addition, the results of the user satisfaction questionnaire indicated a positive mean score of 4.27 ± 0.34 on a Likert scale ranging from 1 to 5.

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In this study, we developed a biosensor based on the localized surface plasmon resonance (LSPR) phenomenon of gold nanoparticles (AuNPs) to detect the widely used herbicide glyphosate in food samples. To do so, either cysteamine or a specific antibody for glyphosate were conjugated to the surface of the nanoparticles. AuNPs were synthesized using the sodium citrate reduction method and had their concentration determined via inductively plasma coupled mass spectrometry. Their optical properties were analyzed using UV-vis spectroscopy, X-ray diffraction, and transmission electron microscopy. Functionalized AuNPs were further characterized via Fourier-transform infrared spectroscopy, Raman scattering, Zeta potential, and dynamic light scattering. Both conjugates succeeded in detecting the presence of glyphosate in the colloid, although nanoparticles functionalized with cysteamine tended to aggregate at high concentrations of the herbicide. On the other hand, AuNPs functionalized with anti-glyphosate functioned at a broad concentration range and successfully identified the presence of the herbicide in non-organic coffee samples and when it was added to an organic coffee sample. This study demonstrates the potential of AuNP-based biosensors to detect glyphosate in food samples. The low-cost and specificity of these biosensors make them a viable alternative to current methods for detecting glyphosate in foodstuffs.

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This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingertips' contact force. Three sensor fabrications were tested for the angle sensor by axially rotating the sensors in four positions. The configuration with the most similar response in the four rotations was chosen. The chosen sensors presented a polynomial response with R2 higher than 92%. The tactile force sensors tracked the force made over the objects. Almost all sensors presented a polynomial response with R2 higher than 94%. The system monitored the prosthesis activity by recognizing grasp types. Six machine learning algorithms were tested: linear regression, k-nearest neighbor, support vector machine, decision tree, k-means clustering, and hierarchical clustering. To validate the algorithms, a k-fold test was used with a k = 10, and the accuracy result for k-nearest neighbor was 98.5%, while that for decision tree was 93.3%, enabling the classification of the eight grip types.

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COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level-SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

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Sensing technologies using optical fibers have been studied and applied since the 1970s in oil and gas, industrial, medical, aerospace, and civil areas. Detecting ultrasound acoustic waves through fiber-optic hydrophone (FOH) sensors can be one solution for continuous measurement of volumes inside production tanks used by these industries. This work presents an FOH system composed of two optical fiber coils made with commercial single mode fiber (SMF) working in the sensor head of a Michelson's interferometer (MI) supported by an active stabilization mechanism that drives another optical coil wound around a piezoelectric actuator (PZT) in the reference arm to mitigate external mechanical and thermal noise from the environment. A 1000 mL glass graduated cylinder filled with water is used as a test tank, inside which the sensor head and an ultrasound source are placed. For detection, amplitudes and phases are measured, and machine learning algorithms predict their respective liquid volumes. The acoustic waves create patterns electronically detected with resolution of 1 mL and sensitivity of 340 mrad/mL and 70 mvolts/mL. The nonlinear behavior of both measurands requires classification, distance metrics, and regression algorithms to define an adequate model. The results show the system can determine liquid volumes with an accuracy of 99.4% using a k-nearest neighbors (k-NN) classification with one neighbor and Manhattan's distance. Moreover, Gaussian process regression using rational quadratic metrics presented a root mean squared error (RMSE) of 0.211 mL.

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Brain-computer interface (BCI) remains an emerging tool that seeks to improve the patient interaction with the therapeutic mechanisms and to generate neuroplasticity progressively through neuromotor abilities. Motor imagery (MI) analysis is the most used paradigm based on the motor cortex's electrical activity to detect movement intention. It has been shown that motor imagery mental practice with movement-associated stimuli may offer an effective strategy to facilitate motor recovery in brain injury patients. In this sense, this study aims to present the BCI associated with visual and haptic stimuli to facilitate MI generation and control the T-FLEX ankle exoskeleton. To achieve this, five post-stroke patients (55-63 years) were subjected to three different strategies using T-FLEX: stationary therapy (ST) without motor imagination, motor imagination with visual stimulation (MIV), and motor imagination with visual-haptic inducement (MIVH). The quantitative characterization of both BCI stimuli strategies was made through the motor imagery accuracy rate, the electroencephalographic (EEG) analysis during the MI active periods, the statistical analysis, and a subjective patient's perception. The preliminary results demonstrated the viability of the BCI-controlled ankle exoskeleton system with the beta rebound, in terms of patient's performance during MI active periods and satisfaction outcomes. Accuracy differences employing haptic stimulus were detected with an average of 68% compared with the 50.7% over only visual stimulus. However, the power spectral density (PSD) did not present changes in prominent activation of the MI band but presented significant variations in terms of laterality. In this way, visual and haptic stimuli improved the subject's MI accuracy but did not generate differential brain activity over the affected hemisphere. Hence, long-term sessions with a more extensive sample and a more robust algorithm should be carried out to evaluate the impact of the proposed system on neuronal and motor evolution after stroke.

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Optical fiber sensors based on fiber Bragg gratings (FBGs) are prone to measurement errors if the cross-sensitivity between temperature and strain is not properly considered. This paper describes a self-compensated technique for canceling the undesired influence of temperature in strain measurement. An edge-filter-based interrogator is proposed and the central peaks of two FBGs (sensor and reference) are matched with the positive and negative slopes of a Fabry-Perot interferometer that acts as an optical filter. A tuning process performed by the grey wolf optimizer (GWO) algorithm is required to determine the optimal spectral characteristics of each FBG. The interrogation range is not compromised by the proposed technique, being determined by the spectral characteristics of the optical filter in accordance with the traditional edge-filtering interrogation. Simulations show that, by employing FBGs with optimal characteristics, temperature variations of 30 °C led to an average relative error of 3.4% for strain measurements up to 700µÏµ. The proposed technique was experimentally tested under non-ideal conditions: two FBGs with spectral characteristics different from the optimized results were used. The temperature sensibility decreased by 50.8% as compared to a temperature uncompensated interrogation system based on an edge filter. The non-ideal experimental conditions were simulated and the maximum error between theoretical and experimental data was 5.79%, proving that the results from simulation and experimentation are compatible.

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Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients' condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject's physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.

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Advances in robotic systems for rehabilitation purposes have led to the development of specialized robot-assisted rehabilitation clinics. In addition, advantageous features of polymer optical fiber (POF) sensors such as light weight, multiplexing capabilities, electromagnetic field immunity and flexibility have resulted in the widespread use of POF sensors in many areas. Considering this background, this paper presents an integrated POF intensity variation-based sensor system for the instrumentation of different devices. We consider different scenarios for physical rehabilitation, resembling a clinic for robot-assisted rehabilitation. Thus, a multiplexing technique for POF intensity variation-based sensors was applied in which an orthosis for flexion/extension movement, a modular exoskeleton for gait assistance and a treadmill were instrumented with POF angle and force sensors, where all the sensors were integrated in the same POF system. In addition, wearable sensors for gait analysis and physiological parameter monitoring were also proposed and applied in gait exercises. The results show the feasibility of the sensors and methods proposed, where, after the characterization of each sensor, the system was implemented with three volunteers: one for the orthosis on the flexion/extension movements, one for the exoskeleton for gait assistance and the other for the free gait analysis using the proposed wearable POF sensors. To the authors' best knowledge, this is the first time that optical fiber sensors have been used as a multiplexed and integrated solution for the simultaneous assessment of different robotic devices and rehabilitation protocols, where such an approach results in a compact, fully integrated and low-cost system, which can be readily employed in any clinical environment.

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This paper presents the development of a smart carpet based on polymer optical fiber (POF) for ground reaction force (GRF) and spatio-temporal gait parameter assessment. The proposed carpet has 20 intensity variation-based sensors on one fiber with two photodetectors for acquisition, each one for the response of 10 closer sensors. The used multiplexing technique is based on side-coupling between the light sources and POF lateral sections in which one light-emitting diode (LED) is activated at a time, sequentially. Three tests were performed, two for sensor characterization and one for validation of the smart carpet, where the first test consisted of the application of calibrated weights on the top of each sensor for force characterization. In the second test, the foot was positioned on predefined points distributed on the carpet, where a mean relative error of 2.9% was obtained. Results of the walking tests on the proposed POF-embedded smart carpet showed the possibility of estimating the GRF and spatio-temporal gait parameters (step and stride lengths, cadence, and stance duration). The obtained results make possible the identification of gait events (stance and swing phases) as well as the stance duration and double support periods. The proposed carpet is a low-cost and reliable tool for gait analysis in different applications.

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We report the development of a fiber Bragg grating (FBG) sensor for multiparameter sensing using only one FBG. The FBG was half-embedded in a 3D-printed structure, which resulted in a division of the grating spectrum creating two peaks with different sensitivities with respect to different physical parameters. A numerical analysis of the proposed technique was performed using the coupled-mode theory with modified transfer matrix formulation. Then, experimental analyses were performed as function of temperature, strain and force, where the peaks showed different sensitivities in all analyzed cases. Such results enable the application of a technique for simultaneous measurement of multiple physical parameters using both peaks and the full width half maximum of the FBG embedded in a 3D structure. In the simultaneous multiparameter assessment, the proposed sensor system was able to estimate the three tested parameters (strain, temperature and force) with relative errors as low as 4%.

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Advances in medicine and improvements in life quality has led to an increase in the life expectancy of the general population. An ageing world population have placed demands on the use of assistive technology and, in particular, towards novel healthcare devices and sensors. Besides the electromagnetic field immunity, polymer optical fiber (POF) sensors have additional advantages due to their material features such as high flexibility, lower Young's modulus (enabling high sensitivity for mechanical parameters), higher elastic limits, and impact resistance. Such advantages are well-aligned with the instrumentation requirements of many healthcare devices and in movement analysis. Aiming at these advantages, this review paper presents the state-of-the-art developments of POF sensors for healthcare applications. A plethora of healthcare applications are discussed, which include movement analysis, physiological parameters monitoring, instrumented insoles, as well as instrumentation of healthcare robotic devices such as exoskeletons, smart walkers, actuators, prostheses, and orthosis. This review paper shows the feasibility of using POF sensors in healthcare applications and, due to the aforementioned advantages, it is possible to envisage a further widespread use of such sensors in this research field in the next few years.

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In this paper, we report the development of a portable energy-efficient interrogator (Perrogator) for wavelength-based optical sensors. The interrogator is based on a compact solution encompassing a white light source and the spectral convolution between the sensor and a tunable filter, which is acquired by a photodetector, where a microcontroller has two functions: (i) To control the filter tuning and to (ii) acquire the photodetector signal. Then, the data is sent to a single-board computer for further signal processing. Furthermore, the employed single-board computer has a Wi-Fi module, which can be used to send the sensors data to the cloud. The proposed approach resulted in an interrogator with a resolution as high as 3.82 pm (for 15.64 nm sweeping range) and maximum acquisition frequency of about 210 Hz (with lower resolution ~15.30 pm). Perrogator was compared with a commercial fiber Bragg grating (FBG) interrogator for strain measurements and good agreement between both devices was found (1.226 pm/µÎµ for the commercial interrogator and 1.201 pm/µÎµ for the proposed approach with root mean square error of 0.0144 and 0.0153, respectively), where the Perrogator has the additional advantages of lower cost, higher portability and lower energy consumption. In order to demonstrate such advantages in conjunction with the high acquisition frequency allowed us to demonstrate two wearable applications using the proposed interrogation device over FBG and Fabry-Perot interferometer (FPI) sensors. In the first application, an FBG-embedded smart textile for knee angle assessment was used to analyze the gait of a healthy person. Due to the capability of reconstructing the FBG spectra, it was possible to employ a technique based on the FBG wavelength shift and reflectivity to decouple the effects of the bending angle and axial strain on the FBG response. The measurement of the knee angle as well as the estimation of the angular and axial displacements on the grating that can be correlated to the variations of the knee center of rotation were performed. In the second application, a FPI was embedded in a chest band for simultaneous measurement of breath and heart rates, where good agreement (error below 5%) was found with the reference sensors in all analyzed cases.

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This paper presents the development and validation of a polymer optical-fiber strain-gauge sensor based on the light-coupling principle to measure axial deformation of elastic tendons incorporated in soft actuators for wearable assistive robots. An analytical model was proposed and further validated with experiment tests, showing correlation with a coefficient of R = 0.998 between experiment and theoretical data, and reaching a maximum axial displacement range of 15 mm and no significant hysteresis. Furthermore, experiment tests were carried out attaching the validated sensor to the elastic tendon. Results of three experiment tests show the sensor's capability to measure the tendon's response under tensile axial stress, finding 20.45% of hysteresis in the material's response between the stretching and recovery phase. Based on these results, there is evidence of the potential that the fiber-optical strain sensor presents for future applications in the characterization of such tendons and identification of dynamic models that allow the understanding of the material's response to the development of more efficient interaction-control strategies.

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We developed a flexible support with embedded polymer optical fiber (POF) sensors for the assessment of human⁻robot interaction forces. The supports were fabricated with a three-dimensional (3D) printer, where an acrylonitrile butadiene styrene (ABS) rigid structure was used in the region of the support in which the exoskeleton was attached, whereas a thermoplastic polyurethane (TPU) flexible structure was printed in the region where the users placed their legs. In addition, fiber Bragg gratings (FBGs), inscribed in low-loss, cyclic, transparent, optical polymer (CYTOP) using the direct-write, plane-by-plane femtosecond laser inscription method, were embedded in the TPU structure. In this case, a 2-FBG array was embedded in two supports for human⁻robot interaction force assessment at two points on the users' legs. Both FBG sensors were characterized with respect to temperature and force; additionally, the creep response of the polymer, where temperature influences the force sensitivity, was analyzed. Following the characterization, a compensation method for the creep and temperature influence was derived, showing relative errors below 4.5%. Such errors were lower than the ones obtained with similar sensors in previously published works. The instrumented support was attached to an exoskeleton for knee rehabilitation exercises, where the human⁻robot interaction forces were measured in flexion and extension cycles.

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This paper demonstrates the use of polymer optical fiber Bragg gratings (POFBGs) for angle measurements over a range of different oscillatory frequencies. The POFBGs are inscribed in low-loss, cyclic transparent amorphous fluoropolymers (CYTOP) and are imprinted using the direct-write, plane-by-plane femtosecond laser inscription method. As the polymer has a viscoelastic response and given that the Young's modulus depends on the oscillatory frequency, a compensation technique for sensor frequency cross-sensitivity and hysteresis is proposed and verified. Results show that the proposed compensation technique is able to provide a root mean squared error (RMSE) reduction of 44%, and a RMSE as low as 2.20° was obtained when compared with a reference potentiometer. The hysteresis reduction provided by the proposed technique is 55%, with hysteresis <0.01. The results presented in this paper can pave the way for movement analysis with POFBG providing higher sensitivity and low hysteresis over a large range of motion frequencies.

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Fiber Bragg gratings are widely used optical fiber sensors for measuring temperature and/or mechanical strain. Nevertheless, the high cost of the interrogation systems is the most important drawback for their large commercial application. In this work, an in-line Fabry-Perot interferometer based edge filter is explored in the interrogation of fiber Bragg grating dynamic measurements up to 5 kHz. Two devices an accelerometer and an arterial pulse wave probe were interrogated with the developed approach and the results were compared with a commercial interrogation monitor. The data obtained with the edge filter are in agreement with the commercial device, with a maximum RMSE of 0.05 being able to meet the requirements of the measurements. Resolutions of 3.6 pm and 2.4 pm were obtained, using the optical accelerometer and the arterial pulse wave probe, respectively.