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Fiber specklegram sensors eliminate the need for complex optical interrogators by using a camera to capture images or videos of the speckle pattern at the end facet of a multimode optical fiber. A fiber specklegram sensor is highly sensitive to movement anywhere along the length of the fiber, yielding complex information on external perturbations. The ability of the fiber specklegram sensor to be laid over a large surface area and respond to movement at any location can be useful in many real life sensing problems. Until now, only static speckle images have been used to evaluate such perturbations. However, there are applications where intrinsically dynamic measurands are desired, requiring different approaches to the analysis. In this work, we utilized deep learning techniques to analyze dynamic speckle videos for applications in dynamic biomechanical sensing, which we demonstrated for respiration rate monitoring as a proof-of-concept. This application demands both wide area sensing, that is, coverage of a mattress, together with dynamic information. We achieve this by recording videos of speckle from a single multimode fiber that covers a mattress in an S-configuration, and train convolutional neural networks directly on the video data. We show that the fiber specklegram sensor combined with our deep learning model can accurately classify the respiration rate. This approach has wide reaching potential for other biomechanical healthcare applications, such as pressure sore prevention and continuous monitoring to reduce the risk of falls.

In this paper, a microfluidic sensor based on suspended core fiber (SCF) for detecting the concentration of human chorionic gonadotropin (hCG) has been presented and experimentally demonstrated. The sensor is made by splicing SCF between two sections of drawn-tapered single-mode fiber (SMF). The SCF is a multimode fiber that supports multi-mode optical transmission and generates mode interference in the fiber core. The air holes in the SCF can also be used as microcirculation channels for the sensor, enhancing the interaction between the detection sample and the light field and improving the refractive index (RI) sensitivity. Due to the closed microchannels, the sensor has good anti-interference and stability, can effectively prevent biological pollution. The sensitivity of RI is 1848 nm/RIU in the RI range of 1.3335-1.3419. The sensor can also detect local RI changes on the fiber surface due to biomolecule binding. The hCG antigen can specifically bind to the hCG-β antibody. The hCG-β antibody is immobilized in the air holes of the SCF to specifically detect the concentration of hCG. Experimental results showed that the sensor can specifically detect hCG concentration with a sensitivity of 1.888 nm/(mIU/ml) and a detection limit of 0.049 mIU/ml at a temperature of 25 °C. This sensor has good specificity and potential applications in medical diagnostics, drug testing, and tumor screening.

Accurate detection of triglycerides (TGs) is important for the prevention and monitoring of dyslipidemia and for understanding the mechanisms of metabolic diseases. In this article, an ultrasensitive label-free sensor for measuring TGs based on an exposed-core optical fiber is presented. The sensor head was formed by splicing a single-mode optical fiber, a multimode optical fiber, and an exposed-core fiber in a Mach–Zehnder interferometer configuration. The Mach–Zehnder interference (MZI) occurs between the exposed core of the fiber, which is only micrometer in size, and the external environment. This allows the light path to pass directly through the liquid sample, resulting in an ultrahigh refractive index sensitivity of 13300 nm/RIU in the refractive index range of 1.331–1.332. In addition, lipase is immobilized on the optical fiber sensor to measure the concentration of TGs by detecting the change in the refractive index of the sample solution caused by the hydrolysis of TGs by lipase. The results showed that this sensor can specifically detect the concentration of TGs with a sensitivity of 1.72 nm/nM and a detection limit of 35 pM. It has the potential to revolutionize the diagnosis and monitoring of TGs and thus impact the prevention and treatment of dyslipidemia and other metabolic diseases.

Optical fiber pressure sensing is of significant interest for industrial process monitoring and acoustic sensing. However, the direct detection of pressure changes along an optical fiber with minimal crosstalk is a significant challenge due to the low stress-optic coefficient of glass and interferences from temperature, bending, and strain. Here, we demonstrate that deep learning combined with microstructured optical fiber specklegram sensing is an effective approach to overcoming environmental crosstalk for the challenging application of pressure sensing. Specklegrams created from multimode interference are inherently sensitive to both the measurand of interest and other environmental perturbations. By employing a deep neural network, namely a multilayer perceptron, we show that the environmental crosstalk of the specklegram-based pressure sensor can be mitigated. Furthermore, we demonstrate the practical implementation of the machine learning-based pressure sensor where we need to continuously update the model. We show that the network can learn and reject environmental disturbances and predict pressure values correctly through continuous training updates. This technique approaches the instrument error of our calibration pressure gauge, with an error below 3 kPa over a range of 1 MPa.

It is well established that plants need a range of soil nutrients to grow. In farming, these nutrients are generally added to the soil in the form of fertilizers. However, depending on the soil conditions (such as temperature, water content, pH, and soil type), nutrients may not be in the right form for plant uptake. Determining the availability of nutrients in the soil for plant growth is therefore critical for the yield and productivity of modern farming. A considerable amount of research and knowledge has been developed that shows the importance of the soil pH on the availability (or not) of nutrients. Furthermore, pH plays a crucial role in controlling the availability of potential toxic elements, such as aluminum and manganese. This review article discusses recent research aimed at real‐time and continuous soil pH measurement in‐situ. More specifically, it focuses on the development of polymer materials that will ultimately enable pH measurements for the specific application of in‐ground pH sensing. Given the breadth of the polymeric sensor research field, this review has a narrowed focus on optical and electrochemical transduction methods.