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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.

The new generation of distributed optical sensors with improved interrogation, multiplexing, and acquisition techniques with the possibility of performing measurements with high spatial resolution over tens of kilometers of optical fiber has led to the accumulation of a vast volume of data that can present a big challenge to process and store all this data. Looking for simple solutions to this problem, we present in this paper a data compression method for distributed temperature sensors. This compression approach performs the spatial derivative of the temperature signal, constituting a simple and effective method to remove redundant information. Also, this compression methodology is suitable for temperature data, as it follows thermal variations over time and can be applied to any temperature profile with multiple thermal events along the sensing fiber, whether in heating or cooling circumstances. Tests performed with a large amount of experimental data showed that an average compression ratio of 1.5× can be obtained by removing redundant spatial temperature variations without losing spatial resolution.

Raman-based distributed temperature sensor (RDTS) devices have grown dramatically over the past two decades, partially driving the optical sensor industry. Over nearly four decades, most academic investigations about RDTS have focused on developing distributed sensor devices operating at the wavelength of 1550 nm, given the low loss of standard single-mode fibers in this spectral region. Certainly, the wavelength of 1550 nm is ideal for long-range sensing applications. However, at this wavelength, the signal-to-noise ratio (SNR) of RDTS systems is degraded, given the low intensity of the measured signals. Looking for simple solutions to improve the SNR of this sensing technology, we discuss in this paper an RDTS operating at the spectral region of 840 nm as an alternative for short-range distributed temperature sensing applications delivering an improved SNR.

The performance of a visible light communication (VLC) system based on power-domain nonorthogonal multiple access (PD-NOMA) is experimentally evaluated in this paper. The simplicity of the adopted nonorthogonal scheme is provided by the fixed power allocation method at the transmitter and the single one-tap equalization executed before the successive interference cancellation at the receiver. The experimental results proved the successful transmission of the PD-NOMA scheme with three users in VLC links of up to 2.5 m, after a proper choice of the optical modulation index. All users achieved error vector magnitude (EVM) performances below forward error correction limits in all evaluated transmission distances. At 2.5 m, the user with the best performance reaches an E V M =2.3 % .

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.

This work presents the performance analysis of a passive optical network (PON) proposal with photonic integrated circuits (PIC). The PON architecture was simulated on MATLAB, focusing on the main functionalities of the optical line terminal, distribution network, and network unity regarded its effects on the physical layer. We show a simulated PIC, implemented on MATLAB through its analytic transfer function equation, used to implement orthogonal frequency division multiplexing in the optical domain to enhance the current optical networks for the 5G New-Radio (NR) scenario. We analyzed the OOK and optical PAM4 compared with phase modulation formats such as the DPSK and DQPSK. All modulation formats can be directly detected for the case in the study, simplifying the reception. Consequently, this work achieved a maximum symmetric transmission capacity of 1.2 Tbps over 90 km of standard single-mode fiber with 128 carriers, 64 carriers for downstream and 64 for upstream, obtained from an optical frequency comb with 0.3 dB flatness. We concluded that phase modulation formats associated with PIC could increase PON capability and push further our current scenario to the 5G new era.

A resource optimization methodology is proposed for application in long range wide area networks (LoRaWANs). Using variable neighborhood search (VNS) and a minimum-cost spanning tree algorithm, it reduces the implementation and the maintenance costs of such low power networks. Performance evaluations were conducted in LoRaWANs with LoRa repeaters to increase coverage, in scenario where the number and the location of the repeaters are determined by the VNS metaheuristic. Parameters such as spread factor (SF), bandwidth and transmission power were adjusted to minimize the network's total energy per useful bit (Ebit) and the total data collection time. The importance of the SF in the trade-off between (Ebit) and time on-air is evaluated, considering a device scaling factor. Simulation results, obtained after model adjustments with experimental data, show that, in networks with few associated devices, there is a preference for small values of SF aiming at reduction of Ebit. The usage of large SF's becomes relevant when reach extensions are required. The results also demonstrate that, for networks with high number of nodes, the scaling of devices over time become relevant in the fitness function, forcing an equal distribution of time slots per SF to avoid discrepancies in the time data collection.

In this work, we analyze different types of recurrent neural networks (RNNs) working under several different parameters to best model the nonlinear optical dynamics of pulse propagation. Here we studied the propagation of picosecond and femtosecond pulses under distinct initial conditions going through 13 m of a highly nonlinear fiber and demonstrated the application of two RNNs returning error metrics such as normalized root mean squared error (NRMSE) as low as 9%. Those results were further extended for a dataset outside the initial pulse conditions used on the RNN training, and the best-proposed network was still able to achieve a NRMSE below 14%. We believe that this study can contribute to a better understanding of building RNNs employed for modeling nonlinear optical pulse propagation and of how the peak power and nonlinearity affect the prediction error.

Measuring cortisol levels as a stress biomarker is essential in many medical conditions associated with a high risk of metabolic syndromes such as anxiety and cardiovascular diseases, among others. One technology that has a growing interest in recent years is fiber optic biosensors that enable ultrasensitive cortisol detection. Such interest is allied with progress being achieved in basic interrogation, accuracy improvements, and novel applications. The development of improved cortisol monitoring, with a simplified manufacturing process, high reproducibility, and low cost, are challenges that these sensing mechanisms still face, and for which solutions are still needed. In this paper, a comprehensive characterization of a D-shaped fiber optic immunosensor for cortisol detection based on surface plasmon resonance (SPR) enabled by gold coating is reported. Specifically, the sensor instrumentation and fabrication processes are discussed in detail, and a simulation with its complete mathematical formalism is also presented. Moreover, experimental cortisol detection tests were performed for a detection range of 0.01 to 100 ng/mL, attaining a logarithmic sensitivity of 0.65 ± 0.02 nm/log(ng/mL) with a limit of detection (LOD) of 1.46 ng/mL. Additionally, an investigation of signal processing is also discussed, with the main issues addressed in order to highlight the best way to extract the sensing information from the spectra measured with a D-shaped sensor., Competing Interests: The authors declare no conflicts of interest., (© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.)

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 700μ ϵ . 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.

In this paper, we propose a new, to the best of our knowledge, technique based on the measurement and analysis of the intensity of the interference pattern as an alternative approach for interrogating liquid-level interferometric fiber sensors. This interrogation is based on calculations that can take into account a vast number of peaks and dips of an interferometric spectrum, allowing the use of such devices as distributed sensors capable of measuring longer-level ranges. Here, liquid-level measurements of up to 120 mm were experimentally obtained with high linearity and a sensitivity of $ - {0.042}\;{\rm dB/mm}$-0.042dB/mm.

In this paper, we report for the first time, to the best of our knowledge, the experimental generation of dark pulses in the 1.5 µm band from a passively $Q$Q-switched fiber laser employing graphite oxide as the saturable absorber, generating tunable microsecond pulses with kHz repetition rates. The graphite oxide samples were obtained by recycling the graphite present in Li-ion batteries used in cell phones through a chemical separation and oxidation process. Sample characterization employing x-ray diffraction, solid-state $ ^{{13}}{\rm C} $ 13 C nuclear magnetic resonance, and Raman spectroscopy showed that the produced graphite oxide exhibited a homogeneously oxidized structure. Dark pulse emission could be observed at a relatively low pump threshold of 35 mW in a short 20 m laser cavity, indicating that the graphite oxide acted as a saturable absorber, significantly enhancing the nonlinearity of the laser cavity. Additionally, dark pulse operation was demonstrated at a high stability with a signal-to-noise ratio of 56 dB and a pulse-to-pulse timing jitter of 159.84 fs.