Characterization of optical fibers doped with nanoparticles for distributed displacement sensing.

High-scattering optical fibers have emerged as a key component in distributed sensing systems, primarily due to their capacity to enhance signal-to-noise ratio. This paper presents an experimental characterization of optical fibers doped with oxide nanoparticles for displacement sensing. They were manufactured using the phase-separation technique and different doping compounds, including calcium, strontium, lanthanum and magnesium. The Rayleigh backscattering (RBS) signatures in time and frequency domains were acquired using an Optical Backscatter Reflectometer (OBR). The maximum representative length, backscattering gain and strain sensitivity were evaluated. The results indicate that the fiber co-doped with magnesium and erbium chlorides offered the best compromise between strain sensitivity (0.96 pm/µ ϵ) and maximum length (17 m). For conditions of single and multiple perturbations, strain saturation was reached at ≥7000 µm and <1500 µm, respectively. In addition, the results reveal that, under a condition of variable temperature (30-60 °C), the sensor response becomes significantly nonlinear over length, requiring a technique for temperature cross-sensitivity mitigation that accounts for nonlinearities in sensitivity and hysteresis.

Increased Risk of Falling in Older Adults When Coordinating Obstacle Avoidance and Grasping.

This study aimed to investigate the kinematic changes in obstacle avoidance and prehension tasks performed simultaneously by older adults with a history of falls at different levels of task difficulty. Twenty-six older adults were divided into faller and nonfaller groups. The experimental protocol was divided into two different tasks: walking with obstacle avoidance and walking with obstacle avoidance combined with a reach-to-grasp task. Two types of sensors (Kinect v2 and Leap Motion Controller, respectively) were used to analyze gait and grasp. Fallers presented kinematic changes associated with the grasping task during obstacle avoidance, such as a decrease in the velocity of the center of mass and the step length, an increase in the step width, a decrease in toe-obstacle horizontal distance, and an increase in vertical foot clearance distance, and an increase in movement time in the grasping task compared with nonfallers. To cope with the obstacle avoidance demands of both walking and grasping, fallers turned to a specific sequencing strategy. While slowing down, they attended first to the grasping task and then to crossing the obstacle on the floor.

POF Smart Pants: a fully portable optical fiber-integrated smart textile for remote monitoring of lower limb biomechanics.

This paper presents the development of an optical fiber-integrated smart textile used as an instrumented pants for biomechanical and activity recognition. The optical fiber sensor is based on the multiplexed intensity variation technique in which a side coupling between a polymer optical fiber (POF) and light sources with controlled modulation is developed. In addition, the sensor system is integrated into pants, where two POFs with 30 sensors each are placed on the left and right legs of the proposed POF Smart Pants. After the device's fabrication and assembly, the 60 optical fiber sensors are characterized as a function of the transverse displacement on the sensor's region. In this case, each sensor presented its sensitivities (108.03 ± 100 mV/mm), which are used on the sensor normalization prior to the data analysis. Then, the tests with volunteer performing different daily activities indicated the suitability of the proposed device on the assessment of biomechanics of human movement in different activities as well as the spatio-temporal parameters of the gait in different velocity conditions. For activity recognition, a neural network is applied and presented 100% accuracy on the activity recognition. Then, to provide an optimization of the number of sensors, the principal components analysis is applied and indicated a threefold reduction of the number of sensors with an accuracy of 99%. Thus, the proposed POF Smart Pants is a feasible alternative for a low-cost and highly reliable sensor system for remote monitoring of different patients, with the possibility of customizing the device for different users.

Elastomer-Embedded Multiplexed Optical Fiber Sensor System for Multiplane Shape Reconstruction.

This paper presents the development and application of a multiplexed intensity variation-based sensor system for multiplane shape reconstruction. The sensor is based on a polymer optical fiber (POF) with sequential lateral sections coupled with a flexible light-emitting diode (LED) belt. The optical source modulation enables the development of 30 independent sensors using one photodetector, where the sensor system is embedded in polydimethylsiloxane (PDMS) resin in two configurations. Configuration 1 is a continuous PDMS layer applied in the interface between the flexible LED belt and the POF, whereas Configuration 2 comprises a 20 mm length PDMS layer only on each lateral section and LED region. The finite element method (FEM) is employed for the strain distribution evaluation in different conditions, including the strain distribution on the sensor system subjected to momentums in roll, pitch and yaw conditions. The experimental results of pressure application at 30 regions for each configuration indicated a higher sensitivity of Configuration 1 (83.58 a.u./kPa) when compared with Configuration 2 (40.06 a.u./kPa). However, Configuration 2 presented the smallest cross-sensitivity between sequential sensors (0.94 a.u./kPa against 45.5 a.u./kPa of Configuration 1). Then, the possibility of real-time loading condition monitoring and shape reconstruction is evaluated using Configuration 1 subjected to momentums in roll, pitch and yaw, as well as mechanical waves applied on the sensor structure. The strain distribution on the sensor presented the same pattern as the one obtained in the simulations, and the real-time response of each sensor was obtained for each case. In addition, the possibility of real-time loading condition estimation is analyzed using the k-means algorithm (an unsupervised machine learning approach) for the clusterization of data regarding the loading condition. The comparison between the predicted results and the real ones shows a 90.55% success rate. Thus, the proposed sensor device is a feasible alternative for integrated sensing in movement analysis, structural health monitoring submitted to dynamic loading and robotics for the assessment of the robot structure.

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.

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 (R2) higher than 0.9.

Development and Evaluation of BenchBalance: A System for Benchmarking Balance Capabilities of Wearable Robots and Their Users.

Recent advances in the control of overground exoskeletons are being centered on improving balance support and decreasing the reliance on crutches. However, appropriate methods to quantify the stability of these exoskeletons (and their users) are still under development. A reliable and reproducible balance assessment is critical to enrich exoskeletons' performance and their interaction with humans. In this work, we present the BenchBalance system, which is a benchmarking solution to conduct reproducible balance assessments of exoskeletons and their users. Integrating two key elements, i.e., a hand-held perturbator and a smart garment, BenchBalance is a portable and low-cost system that provides a quantitative assessment related to the reaction and capacity of wearable exoskeletons and their users to respond to controlled external perturbations. A software interface is used to guide the experimenter throughout a predefined protocol of measurable perturbations, taking into account antero-posterior and mediolateral responses. In total, the protocol is composed of sixteen perturbation conditions, which vary in magnitude and location while still controlling their orientation. The data acquired by the interface are classified and saved for a subsequent analysis based on synthetic metrics. In this paper, we present a proof of principle of the BenchBalance system with a healthy user in two scenarios: subject not wearing and subject wearing the H2 lower-limb exoskeleton. After a brief training period, the experimenter was able to provide the manual perturbations of the protocol in a consistent and reproducible way. The balance metrics defined within the BenchBalance framework were able to detect differences in performance depending on the perturbation magnitude, location, and the presence or not of the exoskeleton. The BenchBalance system will be integrated at EUROBENCH facilities to benchmark the balance capabilities of wearable exoskeletons and their users.

Polymer optical fibers for mechanical wave monitoring.

This Letter presents the development of a low-cost polymer optical fiber (POF) sensor for mechanical wave monitoring. The POF is fabricated using the light polymerization spinning process (LPS-POF) with Bisphenol-A as its main component, resulting in a highly flexible fiber. The proposed LPS-POF sensor is applied on the assessment of squared waves with different amplitudes, where the amplitude and dynamic responses are compared to the ones of a piezoelectric transducer (PZT). In static conditions, a determination coefficient (R2) of 0.990 is obtained between the reference (PZT) and proposed sensors for the amplitude assessment of the wave. In dynamic analysis, the LPS-POF viscoelasticity is compensated using viscoelastic constitutive models, resulting in a R2 of 0.988 between the sensor responses, which indicate a mean error reduction of 21% when compared to the uncompensated responses in the amplitudes of different square waves. The dynamic analysis also shows the sensor capability of operating in frequencies as high as 25 Hz. Then, the sensor's responses, compared to the input squared wave, show the possibility of wave velocity measurement. Therefore, with a LPS-POF sensor array, it is possible to monitor these parameters in practical applications.

Smart textiles for multimodal wearable sensing using highly stretchable multiplexed optical fiber system.

This paper presents the development and application of a multiparameter, quasi-distributed smart textile based on embedded highly stretchable polymer optical fiber (POF) sensors. The POF is fabricated using the light polymerization spinning process, resulting a highly stretchable optical fiber, so-called LPS-POF, with Young's modulus and elastic limits of 15 MPa and 17%, respectively. The differential scanning calorimetry shows a thermal stability of the LPS-POF in temperature range of 13-40 °C. The developed sensors are based on the optical power variation, which results in a fully portable and low-cost technique. In order to obtain a multiplexed sensor system, a technique based on flexible light emitting diodes (LEDs) on-off keying modulation is applied, where each LED represents the response of one sensor. The smart textile comprises of LPS-POF and three flexible LEDs embedded in neoprene textile fabric. The performance of the system is evaluated for temperature, transverse force and angular displacement detection at different planes. The sensors presented high linearity (mean determination coefficient of 0.99) and high repeatability (inter-measurement deviations below 5%). The sensor is also applied in activity detection, where the principal component analysis (PCA) was applied in the sensors responses and, in conjunction with clustering techniques such as k-means, indicate the possibility of detecting basic activities such as walking, sitting on a chair and squatting.

Polymer Optical Fiber-Based Integrated Instrumentation in a Robot-Assisted Rehabilitation Smart Environment: A Proof of Concept.

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.

Performance Analysis of a Lower Limb Multi Joint Angle Sensor Using CYTOP Fiber: Influence of Light Source Wavelength and Angular Velocity Compensation.

This paper presents the analysis of an intensity variation polymer optical fiber (POF)-based angle sensor performance, i.e., sensitivity, hysteresis and determination coefficient ( R 2 ), using cyclic transparent optical polymer (CYTOP) fiber. The analysis consisted of two approaches: influence of different light source central wavelengths (430 nm, 530 nm, 660 nm, 870 nm and 950 nm) and influence of different angular velocities ( 0.70 rad/s, 0.87 rad/s, 1.16 rad/s, 1.75 rad/s and 3.49 rad/s). The first approach aimed to select the source which resulted in the most suitable performance regarding highest sensitivity and linearity while maintaining lowest hysteresis, through the figure of merit. Thereafter, the analysis of different angular velocities was performed to evaluate the influence of velocity in the curvature sensor performance. Then, a discrete angular velocity compensation was proposed in order to reduce the root-mean-square error (RMSE) of responses for different angular velocities. Ten tests for each analysis were performed with angular range of 0 ∘ to 50 ∘ , based on knee and ankle angle range during the gait. The curvature sensor was applied in patterns simulating the knee and ankle during the gait. Results show repeatability and the best sensor performance for λ = 950 nm in the first analysis and show high errors for high angular velocities ( w = 3.49 rad/s) in the second analysis, which presented up to 50 % angular error. The uncompensated RMSE was high for all velocities ( 6.45 ∘ to 12.41 ∘ ), whereas the compensated RMSE decreased up to 74 % ( 1.67 ∘ to 3.62 ∘ ). The compensated responses of application tests showed maximum error of 5.52 ∘ and minimum of 1.06 ∘ , presenting a decrease of mean angular error up to 30 ∘ when compared with uncompensated responses.

POF Smart Carpet: A Multiplexed Polymer Optical Fiber-Embedded Smart Carpet for Gait Analysis.

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.

Polymer Optical Fiber Sensors in Healthcare Applications: A Comprehensive Review.

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.

Perrogator: A Portable Energy-Efficient Interrogator for Dynamic Monitoring of Wavelength-Based Sensors in Wearable Applications.

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.

Design considerations, analysis, and application of a low-cost, fully portable, wearable polymer optical fiber curvature sensor.

This paper presents the development of a low-cost, fully portable, wearable sensor for joint angle assessment based on a polymer optical fiber (POF) curvature sensor. The mechanical support configurations as well as the fiber length are analyzed to obtain a sensor with lower hysteresis and higher sensitivity and linearity. In addition, the annealing is made in the fiber to further reduce the sensor errors, and an analysis to obtain the sensor cross-sensitivity with respect to temperature and relative humidity is performed. Finally, a viscoelastic-based compensation technique is applied on the proposed wearable sensor not only to reduce its hysteresis and errors, but also to increase the sensor linearity. The sensor is validated on flexion and extension cycles with different angular velocities. Results show that the proposed sensor presents root mean squared errors of about 1.5° and mean hysteresis of about 1%. The wearable POF curvature sensor was applied on the angle measurement of an elbow joint during flexion and extension cycles and on the knee during the gait cycle, where high repeatability and low errors also were found.