Biocompatible and resorbable optical fibres emerge as promising technologies for in vivo applications like imaging, light delivery for phototherapy and optogenetics, and localised drug-delivery, as well as for biochemical sensing, wherein the probe can be implanted and then completely absorbed by the organism. Biodegradable waveguides based on glasses, hydrogels, and silk have been reported, but most of these devices rely on complex fabrication procedures. In this sense, this paper proposes a novel structured optical fibre made of agarose, a transparent, edible material used in culture media and tissue engineering. The fibre is obtained by pouring food-grade agar into a mould with stacked rods, forming a solid core surrounded by air holes in which the refractive index and fibre geometry can be tailored by choosing the agarose solution composition and mould design, respectively. Besides exhibiting practical transmittance at 633 nm in relation to other hydrogel waveguides, the fibre is also validated for chemical sensing either by detecting volume changes due to agar swelling/dehydration or modulating the transmitted light by inserting fluids into the air holes. Therefore, the proposed agarose-based structured optical fibre is an easy-to-fabricate, versatile technology with possible applications for medical imaging and in vivo biochemical sensing.

A quantitative study of image matching techniques applied to fiber specklegram sensor analysis is presented. The fiber status is modulated by a microbending transducer, so the output speckle field can be correlated to the input displacements. Once acquired and preprocessed, the specklegrams’ variations were evaluated according to seven approaches. Although the average intensity did not provide reliable information per se, the correlation and sum of differences methods yielded $\sim 11{\mathrm{mm}}^{-1}$ and $\sim 14{\mathrm{mm}}^{-1}$ sensitivities, respectively, within a $\sim 0.06\mathrm{mm}$ range and low linearity and hysteresis errors, with enhancement possibility by intensity level cancellation. Moreover, the phase-only correlation and the mutual information metrics provided very high sensitivities ($22{\mathrm{mm}}^{-1}$ and $120{\mathrm{mm}}^{-1}$, respectively) for a $<0.02\mathrm{mm}$ range, making these techniques suitable for detecting subtle variations in the fiber status due to physical or chemical stimuli.

Fiber specklegram sensors (FSSs) traditionally use statistical methods to analyze specklegrams obtained from fibers for sensing purposes, but can suffer from limitations such as vulnerability to noise and lack of dynamic range. In this paper we demonstrate that deep learning improves the analysis of specklegrams for sensing, which we show here for both air temperature and water immersion length measurements. Two deep neural networks (DNNs); a convolutional neural network and a multi-layer perceptron network, are used and compared to a traditional correlation technique on data obtained from a multimode fiber exposed-core fiber. The ability for the DNNs to be trained against a random noise source such as specklegram translations is also demonstrated.

An optical fiber specklegram sensor interrogation method based on speckle pattern fragmentation is presented. The acquired specklegram images are divided in a square grid, creating sub-images that are further processed by a correlation technique, allowing the quantification of localized changes in the specklegrams. The methodology was tested on the assessment of linear displacements using a microbending transducer, by evaluating different grid sizes. For a $5\times 5$ grid, a $2.53{\mathrm{mm}}^{-1}$ sensitivity over a 0.27 mm range was obtained, representing an extension of 237.5% in comparison to the standard interrogation technique. Therefore, the presented technique allows enhancing the sensor dynamic range without modifying the experimental setup.

The development of a low-cost and flexible optical fiber transducer for measurement of angular displacements is reported. The light intensity attenuation due to fiber microbending losses is correlated to the variations in flexing angle, yielding to a sensitivity of 1.80°. The device was also mounted in a fabric glove to the monitoring of flexion and abduction movements of index and thumb fingers. Once calibrated by a simple procedure, the glove-based system was capable to measure the angular positions with average errors <;5° and 7° for interphalangeal and metacarpophalangeal joints, respectively. Additionally, the repeatability analysis resulted in average range and standard deviations of 8.06° and 3.45°, respectively. The optical fiber sensor provides a low-cost alternative to the real-time monitoring of hand posture, and can be suitable for applications in human-robot and human-computer interactions.