A singlemode-multimode-singlemode (SMS) fiber structure consists of a short section of multimode fiber fusion-spliced between two SMS fibers. The mechanism underpinning the operation of an SMS fiber structure is multimode interference and associated self-imaging. SMS structures can be used in a variety of optical fiber systems but are most commonly used as sensors for a variety of parameters, ranging from macro-world measurands such as temperature, strain, vibration, flow rate, RI and humidity to the micro-world with measurands such as proteins, pathogens, DNA, and specific molecules. While traditional SMS structures employ a short section of standard multimode fiber, a large number of structures have been investigated and demonstrated over the last decade involving the replacement of the multimode fiber section with alternatives such as a hollow core fiber or a tapered fiber. The objective of replacing the multimode fiber has most often been to allow sensing of different measurands or to improve sensitivity. In this paper, several different categories of SMS fiber structures, including traditional SMS, modified SMS and tapered SMS fiber structures are discussed with some theoretical underpinning and reviews of a wide variety of sensing examples and recent advances. The paper then summarizes and compares the performances of a variety of sensors which have been published under a number of headings. The paper concludes by considering the challenges faced by SMS based sensing schemes in terms of their deployment in real world applications and discusses possible future developments of SMS fiber sensors.

A dual-channel single-mode-multi-mode-single-mode (SMS) fiber optic sensor encapsulated by polydimethylsiloxane (PDMS) was proposed for the first time, for the simultaneous monitoring of the brachial and radial arteries for accurate blood pressure prediction. With the help of the machine learning algorithm Support Vector Regression (SVR), the SMS fiber sensor can continuously and accurately monitor the systolic and diastolic blood pressure. Commercial sphygmomanometers are used to calibrate the accuracy of blood pressure measurement. Compared with the single-channel system, this system can extract more pulse wave features for blood pressure prediction, such as radial artery transit time (RPTT), brachial artery transit time (BPTT), and the transit time difference between the radial artery and the brachial artery (DBRPTT). The results show that the performance of dual-channel blood pressure monitoring is more accurate than that of single-channel blood pressure monitoring in terms of the absolute value of the correlation coefficient (R) and the average value of the difference between SBP and DBP. In addition, both the single-channel and dual-channel blood pressure monitoring are in line with the Association for the Advancement of Medical Devices (AAMI), but the average deviation (DM, 0.06 mmHg) and standard deviation (SD, 1.54 mmHg) of dual-channel blood pressure monitoring are more accurate. The blood pressure monitoring system has the characteristics of low cost, high sensitivity, non-invasive and capability for remote real time monitoring, which can provide effective solution for intelligent health monitoring in the era of artificial intelligence in the future.

A low cost, wearable textile-based respiratory sensing system is proposed and experimentally demonstrated. A highly sensitive D-shaped plastic optical fiber (POF) sensor that responds to bending is integrated into an elastic band structure to form a respiratory sensing system. The curvature sensing experiments were conducted on the D-shaped POF sensor, which has a coefficient of determination (R ² ) of 0.9977. The system can be used to monitor not only the respiratory rate (RR) of the human body under different movement states (resting, walking, and running) but also the RR of steady and unsteady respiratory signals due to different physiological states. In addition, using the proposed signal processing technique, the interference of motion noise can be removed and the influence of body movement on the sensor response can be eliminated. The advantages of the system are its low cost, compactness, and simplicity in design. Thus, the application of the proposed respiratory sensing system provides a simple and inexpensive optical solution for wearable health.

For distributed fiber optical sensing based on Sagnac effect, the intrusion is usually located by notch frequency. However, the notch spectrum is the comprehensive result of the intrusion, so when multiple disturbances simultaneously intrude from different positions of the sensing fiber, it is impossible to establish a mathematical expression between the intrusion position and the notch frequency, this leads to the problem of multi-point intrusion localization. Therefore, in this paper, deep learning technology is used to locate multiple disturbing points in Sagnac distributed optical fiber sensing system, and the related specific technologies of deep learning applying to Sagnac distributed optical fiber sensing are studied. First, according to the characteristics of the system, a network structure based on the regression probability distribution is proposed, second, a loss function is constructed. The results show that the trained model can realize the positioning of multiple and single intrusion points.

A new U-shape panda polarization-maintaining fiber (PPMF) based microfiber interferometer coated with graphene oxide (GO) film was proposed and experimentally demonstrated for relative humidity (RH) sensing. Experimental results show that the U-shape sensor has refractive index (RI) sensitivity of 1692.5 nm/RIU in the RI range of 1.33 when the diameter of the taper waist is 10.08 μm. The surface of the U-shape sensor was then modified chemically and coated with a thin layer of GO film (59.64 nm) for RH detection and the sensitivity is proportional to RH: as RH increases from 30% to 98%, the sensitivity increases from 0.111 to 0.361 nm/%RH and the response time is 0.28 s. In addition, the cross sensitivity to temperature, stability, reproducibility, and response/recovery time of the RH sensor were studied in detail. The proposed U-shape fiber RH sensor has advantage of high sensitivity, good reproducibility and fast response (0.28 s), which has potential application in areas requiring dynamic measurement of RH variations such as industrial product fabrication process control and breath state monitoring.

A high temperature (up to 950 °C) sensor was proposed and demonstrated based on a micro taper in-line fiber Mach–Zehnder interferometer (MZI) structure. The fiber MZI structure comprises a single mode fiber (SMF) with two micro tapers along its longitudinal direction. An annealing at 1000 °C was applied to the fiber sensor to stabilize the temperature measurement. The experimental results showed that the sensitivity was 0.114 nm/°C and 0.116 nm/°C for the heating and cooling cycles, respectively, and, after two days, the sensor still had a sensitivity of 0.11 nm/°C, showing a good stability of the sensor. A probe-type fiber MZI was designed by cutting the sandwiched SMF, which has good linear temperature responses of 0.113 nm/°C over a large temperature range from 89 to 950 °C. The probe-type fiber MZI temperature sensor was independent to the surrounding refractive index (RI) and immune to strain. The developed sensor has a wide application prospect in the fields of high temperature hot gas flow, as well as oil and gas field development.