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.

Respiration rate (RR) is an important information related to human physiological health. A wearable optical fiber sensor for respiration monitoring based on a bend singlemode-multimodesinglemode (SMS) fiber structure, which is highly sensitive to bend, is firstly proposed and experimentally demonstrated. The sensor fastened by an elastic belt on the abdomen of a person will acquire the respiration signal when the person breaths, which will introduce front and back movement of the abdomen, and thus bend of SMS fiber structure. Short-time Fourier transform (STFT) method is employed for signal processing to extract characteristic information of both the time and frequency domain of the measured waveform, which provides accurate RR measurement. Six different SMS fiber sensors have been tested by six individuals and the experimental results demonstrated that the RR signals can be effectively monitored among different individuals, where an average Pearson Correlation Coefficient of 0.88 of the respiration signal has been achieved, which agrees very well with that of commercial belt respiration sensor. The proposed technique can provide a new wearable and portable solution for monitoring of respiratory with advantage of easy fabrication and robust to environment.

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.

Gesture recognition has always been an important research direction in the field of human–computer interaction. In this article, a wearable gesture recognition system based on a D-shaped plastic optical fiber (POF) curvature sensor is proposed and experimentally studied. A highly bend-sensitive D-shaped POF curvature sensor is made and integrated into a five-channel signal acquisition system on a printed circuit board (8 × 4.5 cm ² ), which is embedded into an elastic glove to collect fingers’ movement data. A total of 13 gestures and 11 grasping actions are defined, and the gesture data, the grasping action data, and the gesture data mixed with grasping action data are normalized, calibrated, and imported into a support vector machine (SVM) classifier based on the Gaussian kernel function and feedforward neural networks (FNN), respectively. The recognition accuracy based on the SVM for the 13 gestures and 11 grasping actions reaches 99.8% and 97.7%, respectively. The recognition accuracy of 13 kinds of gesture data mixed with 11 kinds of grasping action data based on the Gaussian kernel function in the SVM classification model and FNN is 98.9% and 99.4%, respectively.

φ-OTDR technology can transform the fiber optic cable into a large-scale sensor array for distributed acoustic sensing (DAS), which is an emerging infrastructure for the Internet of Things. However, it’s limitated in event recognition capability, which is a major factor preventing its practical field application. This paper proposes a perturbation recognition algorithm based on GASF-ConvNeXt-TF with fast process and high recognition accuracy. Firstly, GASF (Gramian angular summation field) algorithm is used to encode external disturbance signal to transform the one-dimensional time series signal into a more concentrated two-dimensional image feature. Then the CNN model ConvNeXttiny network is applied as the classifier. In order to prevent the weight gradient from oscillating back and forth during network training process, a cosine annealing algorithm is introduced to control the decay of the learning rate. Meanwhile, transfer learning is used to further optimize the network model, resulting in higher classification accuracy and faster convergence. Finally, two different experimental scenarios are arranged in a total length of 2.2 kilometers of optical fiber cable, and six different disturbance events (shaking, kicking, knocking, trampling, wheel rolling, and impacting) are set. Different from previous perimeter security disturbance identification experiments, not only single-point disturbance recognition is performed, but also two points disturbances are simultaneously recognized, and all have good overall identification accuracy. The overall recognition accuracy of the six disturbance events in single and multiple points experiments are 99.3% and 98.3%, respectively, with an average recognition time of 0.103s. The proposed technique has potential application in infrastructures structure health monitoring, such as factories, airports, energy pipeline and highway.