Polymer Optical Fiber Sensors Research Articles

Soft Polymer Optical Fiber Sensors for Intelligent Recognition of Elastomer Deformations and Wearable Applications.

In recent years, soft robotic sensors have rapidly advanced to endow robots with the ability to interact with the external environment. Here, we propose a polymer optical fiber (POF) sensor with sensitive and stable detection performance for strain, bending, twisting, and pressing. Thus, we can map the real-time output light intensity of POF sensors to the spatial morphology of the elastomer. By leveraging the intrinsic correlations of neighboring sensors and machine learning algorithms, we realize the spatially resolved detection of the pressing and multi-dimensional deformation of elastomers. Specifically, the developed intelligent sensing system can effectively recognize the two-dimensional indentation position with a prediction accuracy as large as ~99.17%. The average prediction accuracy of combined strain and twist is ~98.4% using the random forest algorithm. In addition, we demonstrate an integrated intelligent glove for the recognition of hand gestures with a high recognition accuracy of 99.38%. Our work holds promise for applications in soft robots for interactive tasks in complex environments, providing robots with multidimensional proprioceptive perception. And it also can be applied in smart wearable sensing, human prosthetics, and human-machine interaction interfaces.

“Focus on sales by developing something unique”

AbstractEPIC's technology manager Antonio Castelo met Bjorn Andersen from a Danish developer of novel polymer optical fiber sensor systems.

Incipient Biofouling Detection via Fiber Optical Sensing and Image Analysis in Reverse Osmosis Processes.

Reverse osmosis (RO) is a widely used membrane technology for producing process water or tap water that is receiving increased attention due to water scarcity caused by climate change. A significant challenge in any membrane filtration is the presence of deposits on the membrane surfaces, which negatively affect filtration performance. Biofouling, the formation of biological deposits, poses a significant challenge in RO processes. Early detection and removal of biofouling are essential for effective sanitation and prevention of biological growth in RO-spiral wound modules. This study introduces two methods for the early detection of biofouling, capable of identifying initial stages of biological growth and biofouling in the spacer-filled feed channel. One method utilizes polymer optical fibre sensors that can be easily integrated into standard spiral wound modules. Additionally, image analysis was used to monitor and analyze biofouling in laboratory experiments, providing a complementary approach. To validate the effectiveness of the developed sensing approaches, accelerated biofouling experiments were conducted using a membrane flat module, and the results were compared with common online and offline detection methods. The reported approaches enable the detection of biofouling before known online parameters become indicative, effectively providing an online detection with sensitivities otherwise only achieved through offline characterization methods.

Respiration frequency rate monitoring using smartphone-integrated polymer optical fibers sensors with cloud connectivity

This paper presents a respiratory frequency system composed by a polymer optical fiber sensor using a smartphone as the interrogator and a remote access to information through a Web server. The system developed a locally-based signal processing method for a dedicated designed application while ensuring remote access using the Thingspeak Web server and application. To check the sensor accuracy, tests with known respiratory rates of 30, 35 and 40 Breaths per Minute (BPM), respectively, are performed using a metronome. The sensor is manually stretched on the metronome beats frequency and the results achieved by the system were of 30.3599, 35.88 and 41.4 BPM. Furthermore, tests are performed in volunteers to check system performance in a real environment, where the volunteers are asked to breathe normally so their respiratory rate could be counted manually and compared to the values delivered by the experimental system in a given period. The error rate verified in the validation experiments are less than 2%. To verify the remote access to information, it is checked whether the respiratory rate data measured by the acquisition system is accordingly sent and visualized by Thingview: the application used in this prototype. The frequency read on the app is the same as measured by the sensor, which shows the suitability of the proposed approach for remote sensing applications with cloud integration.

Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis

In the present scenario like COVID-19 pandemic, to maintain physical distance, the gait-based biometric is a must. Human gait identification is a very difficult process, but it is a suitable distance biometric that also gives good results at low resolution conditions even with face features that are not clear. This study describes the construction of a smart carpet that measures ground response force (GRF) and spatio-temporal gait parameters (STGP) using a polymer optical fiber sensor (POFS). The suggested carpet contains two light detection units for acquiring signals. Each unit obtains response from 10 nearby sensors. There are 20 intensity deviation sensors on a fiber. Light-emitting diodes (LED) are triggered successively, using the multiplexing approach that is being employed. Multiplexing is dependent on coupling among the LED and POFS sections. Results of walking experiments performed on the smart carpet suggested that certain parameters, including step length, stride length, cadence, and stance time, might be used to estimate the GRF and STGP. The results enable the detection of gait, including the swing phase, stance, stance length, and double supporting periods. The suggested carpet is dependable, reasonably priced equipment for gait acquisition in a variety of applications. Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. GA- and PSO-based gait template analyses are performed to extract the features with respect to the gait signals obtained from polymer optical gait sensors (POGS). The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. The results obtained using both classifiers are compared.

Development of Fe-C film coated polymer optical fiber sensor for steel bar corrosion monitoring

Corrosion detection is one of the most important studies in structural health monitoring systems (SHM) for reinforced concrete structures such as bridges. Due to the high initial investment and technical limitations of conventional technology, SHM systems are mostly deployed in the existing mega-scaled bridge infrastructures. This work demonstrates a novel nondestructive method using Fe-C film-coated polymer optical fiber sensor for steel bar corrosion monitoring. The sensing performance of the proposed sensors was investigated in the simulated corrosion test. The results showed that the sensors with different thicknesses of Fe-C film shared similar output curve characteristics but different sensitivities to the same corrosion state. The sensors embedded in the mortar specimens with the reinforced structure were then evaluated under the accelerated corrosion condition. The results indicated that the output signals of sensors were in agreement with that of the earlier experiment, and the thinner Fe-C film showed higher sensitivity to corrosion.

Temperature-insensitive water content estimation in oil-water emulsion using POF sensors

This paper presents the development of an optical fiber sensor for water content estimation in oil–water emulsions. The sensor is based on the surface Plasmon resonance (SPR) principle applied in a polymer optical fiber (POF). For the sensor fabrication, a D-shape is performed on the POF to expose its core region, which has a gold thin film (nanometer thickness) for the SPR signal. The sensor is analyzed as a function of the water content in different emulsions conditions (from 0 to 55.5 M of water content), where a sensitivity of − 0.0288 nm/M was obtained. In addition, the sensor presented a sensitivity of the transmitted optical power variation as a function of the water content. These results lead to the possibility of data fusion between optical power and wavelength responses to obtain a high accuracy in the water content estimation. In this case, a root mean squared error (RMSE) of 1.36 M was obtained on the emulsion characterization. As an important drawback in general sensors applications, the temperature cross-sensitivity was also analyzed, where a sensitivity of 0.118 nm/°C was obtained. Such temperature sensitivity of the sensor leads to errors in emulsion characterization as a function of the temperature. To address this issue, a compensation method based on direct difference between the sensor’s sensitivities is proposed. By applying the proposed compensation method, a reduction of the temperature-induced errors in the water content estimation from 6.46 %±7.81 % to only 0.32 %±1.96 %.

High-Fidelity Strain and Temperature Measurements of Li-Ion Batteries Using Polymer Optical Fiber Sensors

The convergence of fiber optic sensing with lithium-ion batteries holds great promise for observing key cell parameters in real time, which is essential to every level of decision making, from design and engineering to finance and management. Optical sensors based on fiber Bragg gratings have recently been demonstrated as an ideal tool for measuring these metrics with sufficient temporal and spatial resolution. In this work, we extend the use of fiber Bragg gratings to polymeric optical fibers which have notably greater thermal and strain coefficients than their common silica counterparts. We demonstrate that a polymer optical fiber sensor paired with a silica-based sensor, both affixed to the external package of a lithium battery, can concurrently generate high fidelity temperature and volumetric expansion data through this non-invasive approach. The quality of this data allows for further assessments as mechanical characteristics associated with dimensional changes of cells may indicate more than simple charging or discharging during cycling. While internal monitoring remains essential for future diagnostics, external monitoring using polymer fiber sensors offers a straightforward, superficial, and cost-effective sensing solution that opens a new avenue for real-time cell assessment, prognostics, and packaging considerations.

Temperature and Humidity Sensitivity of Polymer Optical Fibre Sensors Tuned by Pre-Strain.

Polymer optical fibre Bragg grating (POFBG) sensors are of high interest due to their enhanced fracture toughness, flexibility in bending, and sensitivity in stress and pressure monitoring applications compared to silica-based sensors. The POFBG sensors can also detect humidity due to the hydrophilic nature of some polymers. However, multi-parameter sensing can cause cross-sensitivity issues in certain applications if the temperature and humidity measurements are not adequately compensated. In this work, we demonstrate the possibility of selectively tuning sensors’ temperature and humidity sensitivities to the desired level by applying a certain amount of fibre pre-strain. The temperature sensitivity of POFBG sensors fabricated in perfluoropolymers (CYTOP) can be selectively tuned from positive to negative values, having the option for insensitivity in specific temperature ranges depending on the amount of the applied pre-strain. The humidity sensitivity of sensors can also be changed from positive values to insensitivity. The importance of thermal annealing treatment of POFBG sensors for improved repeatability in temperature measurements is also reported. An array of 4 multiplexed POFBGs was fabricated, and each sensor was pre-strained accordingly to demonstrate the possibility of having targeted temperature and humidity sensitivities along the same fibre.

Low-cost plastic optical fiber integrated with smartphone for human physiological monitoring

Human physiological signal monitoring is a key factor in diagnosing health conditions, such as monitoring heart rate and respiration. To meet the increasing monitoring requirements, such as safety, economy, and equipment miniaturization, it is necessary to optimize and improve the monitoring device. Polymer optical fiber sensor has great potential in human physiological monitoring because of their inherent safety, compactness, electrical isolation, and flexibility. In this paper, we report the integration of low-cost plastic optical fibers in smartphones for the measurement of respiratory rate and heart rate in human physiological monitoring. The performance of the plastic optical fiber (POF) sensor with different sensitivity zone is analyzed. With three sensitivity zones, the respiratory rate and heart rate can be displayed in the power spectral density simultaneously. For improving the POF sensor performance, the shutter speed of the smartphone is also optimized to 30 s−1. Finally, the POF sensor is tested under different motion states indicate that the sensor can monitor the heart rate and breathing rate under different postures (running, walking, standing, squatting and lying), where the spectral energy is 2.8 ± 0.11 × 108 pt during running and is 1.4 ± 0.55 × 107 pt in the supine state. And it also can be used to assess the dynamic posture, which is important for some potential sensory applications in clinical and home settings.

AI-enabled photonic smart garment for movement analysis

Smart textiles are novel solutions for remote healthcare monitoring which involve non-invasive sensors-integrated clothing. Polymer optical fiber (POF) sensors have attractive features for smart textile technology, and combined with Artificial Intelligence (AI) algorithms increase the potential of intelligent decision-making. This paper presents the development of a fully portable photonic smart garment with 30 multiplexed POF sensors combined with AI algorithms to evaluate the system ability on the activity classification of multiple subjects. Six daily activities are evaluated: standing, sitting, squatting, up-and-down arms, walking and running. A k-nearest neighbors classifier is employed and results from 10 trials of all volunteers presented an accuracy of 94.00 (0.14)%. To achieve an optimal amount of sensors, the principal component analysis is used for one volunteer and results showed an accuracy of 98.14 (0.31)% using 10 sensors, 1.82% lower than using 30 sensors. Cadence and breathing rate were estimated and compared to the data from an inertial measurement unit located on the garment back and the highest error was 2.22%. Shoulder flexion/extension was also evaluated. The proposed approach presented feasibility for activity recognition and movement-related parameters extraction, leading to a system fully optimized, including the number of sensors and wireless communication, for Healthcare 4.0.

Respiratory fabric sensor based on the side luminescence and photosensitivity mechanism of polymer optical fibers.

It is significant to monitor respiration conveniently and in real time for people suffering from respiratory diseases. Polymer optical fibers (POFs) have the advantages of flexibility and light weight, which is highly desirable for wearable respiratory monitoring. However, in most current applications, the POFs are stitched on the textile substrates in the form of macro-bending. This method is complex to fix the bending with certain curvatures and uncomfortable compared with the POF sensors woven into the textile. In this paper, a respiratory fabric sensor based on the side luminescence and photosensitivity mechanism of POF is proposed and demonstrated. The 750µm-diameter POFs were woven into a fabric as warp and laser marking was performed at their designed positions to make them release or couple light. The spacing change between the POFs caused by the respiratory movement accordingly makes the light intensity change in the photosensitive fiber. We chose four fabric widths (10cm, 8cm, 6cm and 4cm) and four fabric weaves (plain weave, honeycomb weave, 1/3 right twill weave and 8/3 warp satin weave) to implement the full-factor experiment for exploring the measurement effect of the respiratory fabric sensor. The result is that the fabric with width of 4cm and weave of 8/3 warp satin is optimal. The calm and deep respiratory tests of the human chest and abdomen in sitting and standing posture were carried out and the test performance of the fabric sensor is almost comparable to that of the medical monitor. The proposed respiratory fabric sensor is comfortable, easily woven and high in precision, which is expected to realize industrialized scale production.

Intensity-Modulated Polymer Optical Fiber-Based Refractive Index Sensor: A Review.

The simple and highly sensitive measurement of the refractive index (RI) of liquids is critical for designing the optical instruments and important in biochemical sensing applications. Intensity modulation-based polymer optical fiber (POF) RI sensors have a lot of advantages including low cost, easy fabrication and operation, good flexibility, and working in the visible wavelength. In this review, recent developments of the intensity modulation POF-based RI sensors are summarized. The materials of the POF and the working principle of intensity modulation are introduced briefly. Moreover, the RI sensing performance of POF sensors with different structures including tapered, bent, and side-polished structures, among others, are presented in detail. Finally, the sensing performance for different structures of POF-based RI sensors are compared and discussed.

Influence of Two-Plane Position and Stress on Intensity-Variation-Based Sensors: Towards Shape Sensing in Polymer Optical Fibers.

Shape reconstruction is growing as an important real-time monitoring strategy for applications that require rigorous control. Polymer optical fiber sensors (POF) have mechanical properties that allow the measurement of large curvatures, making them appropriate for shape sensing. They are also lightweight, compact and chemically stable, meaning they are easy to install and safer in risky environments. This paper presents a sensor system to detect angles in multiple planes using a POF-intensity-variation-based sensor and a procedure to detect the angular position in different planes. Simulations are performed to demonstrate the correlation between the sensor’s mechanical bending response and their optical response. Cyclic flexion experiments are performed at three test frequencies to obtain the sensitivities and the calibration curves of the sensor at different angular positions of the lateral section. A Fast Fourier Transform (FFT) analysis is tested as a method to estimate angular velocities using POF sensors. The experimental results show that the prototype had high repeatability since its sensitivity was similar using different test frequencies at the same lateral section position. The proposed approach proved itself feasible considering that all linear calibration curves presented a coefficient of determination () higher than 0.9.

Polymer Optical Fiber-Based Smart Garment for Impact Identification and Balance Assessment

This paper presents a development of the smart garment instrumented with polymer optical fiber (POF) sensors to identify the impact location during a perturbation protocol for balance assessment. The sensor system installed on the smart garment consists of 30 multiplexed sensors, and each sensor is composed of a light-emitting diode (LED) coupled to a lateral cut in the POF. The sensor's responses are acquired by four photodetectors and the data processing is made by a microcontroller. Two tests were performed to characterize the system. First, forces were applied on each sensor using a reference force sensor to characterize their sensitivities. Second, several perturbations on predefined body areas were performed. A technique based on the sensors' responses is proposed to identify impacts. Finally, the proposed system was applied on a perturbation protocol to assess the human balance under an instability condition. Results of the first test showed different sensor sensitivities due to differences provoked by the manufacturing process, and the sensors were normalized. Results of the second test showed the ability of identifying the impact region by using the impact location identification technique. Furthermore, results of the perturbation protocol showed the feasibility of the proposed system in the balance assessment, in which the identified impact regions combined with the trunk angles obtained by an inertial measurement unit (IMU) improved the balance assessment. The proposed system is portable, low cost, with simple signal processing and ease of implementation, with possibility of scalability, in which can be adapted for other impact detection protocols.

Characterization and analysis of a POF sensor embedded in different materials: Towards wearable systems for stiffness estimation

This paper presents the development, characterization and analysis of an intensity variation-based polymer optical fiber (POF) force sensor using cyclic transparent optical polymer (CYTOP) fiber through side-coupling light source embedded in different materials. Tests with application of vertical force on the sensor with different supports were performed to analyze the sensor responses when embedded in different materials. In addition, vertical force tests along the fiber were performed to analyze the sensor feasibility of detecting the location of applied force. Results of the force characterization on different materials show that the POF-CYTOP force sensor response is related to elastic modulus of the materials. Using the support I, more flexible (elastic modulus of 18.6MPa), the sensor responses presented a high influence of the light coupling, resulting in an increase of the optical power. In contrast, the responses using the support II (elastic modulus of 1.6GPa), presented a decrease of optical power variation with only a minor increase in the optical power when small forces are applied. This is due to the higher strain in the POF, resulting in optical power decrease due to radiation losses and stress-optical effect. Tests with application of the force along the fiber show that the sensor had different sensitivities for impacts located at different distances of the lateral section, leading to the possibility of estimating the impact location using a multiplexed sensor array. The differences in the sensor’s responses over the different supports show the feasibility of using the proposed sensor system for the stiffness assessment in users, where wearable optical fiber sensors can be employed on the real-time estimation of soft tissues, which finds important applications in rehabilitation as well as wearable robotics control and design.

Distributed polymer optical fiber sensors: a review and outlook

Aging degradation and seismic damage of civil infrastructures have become a serious issue for society, and one promising technology for monitoring their conditions is optical fiber sensing. Glass optical fibers have been predominantly used for the past several decades to develop fiber sensors, but currently polymer or plastic optical fibers (POFs) have also been used extensively to develop advanced fiber sensors because of their unique features, such as high flexibility, large breakage strain, and impact resistance. This review focuses on recently developed distributed and quasi-distributed POF-based sensing techniques based on Rayleigh scattering, Brillouin scattering, and fiber Bragg gratings.

Proof of concept: POF enabled neonatal movement sensor system

This paper describes the proof-of-concept development and build of a Polymer Optical Fibre (POF) sensor-based measurement system which detects subtle neonatal movement and other physiological parameters, through a non-contact POF sensor embedded in a mat under the incubator mattress [1].

Recent developments in polymer optical fiber strain sensors: A short review

Polymer optical fiber (POF) strain sensors have attracted increasing attention owing to the unique features of polymer over silica such as lower Young's modulus, larger elastic strain limit, higher fracture toughness, biocompatibility, and lower production cost. Several POF strain sensors have been developed in recent years for applications in various technological fields, including structural health monitoring in civil construction and aerospace industries, biomedicine, and robotics. The principles of operation, prospects, and challenges of the POF strain sensors are discussed in this article under five broad categories: POF fiber Bragg grating sensors, intensity-based POF sensors, multimodal interference-based sensors, Brillouin-frequency-based sensors, and Fabry–Perot cavity sensor. This review aims to highlight areas where further research is required for improving the performance and operating range of POF strain sensors.

Temperature monitoring using polymer optical fiber with integration to the internet of things

In this paper, a polymer optical fiber (POF) is applied for temperature measuring of water solution using a light source of 650nm. The aim is to analyse the impact of temperature variations on the output of POF sensor device in terms of output power and sensitivity. From the study, the POF sensor shows a linear trend when the temperature is increased from 30°C to 80°C with the sensitivity of the polymer optical fiber for output power to the temperature are 0.00973 dBm/°C or 0.14797 nW/°C, for optical characterization and 0.0011 V/°C for electrical characterization. The integration of the Internet of Things to the system helps the user to monitor the temperature of various spaces anywhere at any time. The sensed values are controlled by Arduino Uno R3 and then sent to Blynk to provide wireless monitoring by the user.