In the article, a novel reflective all-fiber integrated sensor for simultaneous gas pressure and temperature sensing has been proposed and demonstrated in experiment. The proposed sensor is fabricated by combining seven core fibers (SCFs), a twin-hole and dual-core fiber (THDCF), a single-mode fiber (SMF) extrinsic Fabry-Perot interferometer (FPI) and a fiber Bragg grating (FBG). The FPI section and FBG section act as a pressure and temperature sensing unit. Especially, the MZI has the effect of filtering the FPI spectrum and is more conducive to the demodulation of the FPI reflected signal. In FPI section, the two SMFs connects with a hollow core capillary (HCC) by the ultraviolet (UV) curing glue. The sensor exhibits a high pressure and temperature sensitivity of 13.10 nm/MPa and 0.20 nm/°C in the range of 0.1-1.2 MPa and 30-90 °C, respectively. The FBG works as a temperature sensor without pressure responding, is fabricated by using the UV laser-based phase mask technology in our lab. By utilizing the dual-wavelength matrix method, both gas pressure and temperature can be measured simultaneously. In sum, with the advantage of compact size, easy fabricated, low cost, high sensitivity, reflection-type and the ability of multi-parameter measurement, our sensor is more competitive in harsh environments.

Driven by the demand to largely mitigate nosocomial infection problems in combating the coronavirus disease 2019 (COVID-19) pandemic, the trend of developing technologies for teleoperation of medical assistive robots is emerging. However, traditional teleoperation of robots requires professional training and sophisticated manipulation, imposing a burden on healthcare workers, taking a long time to deploy, and conflicting the urgent demand for a timely and effective response to the pandemic. This paper presents a novel motion synchronization method enabled by the hybrid mapping technique of hand gesture and upper-limb motion (GuLiM). It tackles a limitation that the existing motion mapping scheme has to be customized according to the kinematic configuration of operators. The operator awakes the robot from any initial pose state without extra calibration procedure, thereby reducing operational complexity and relieving unnecessary pre-training, making it user-friendly for healthcare workers to master teleoperation skills. Experimenting with robotic grasping tasks verifies the outperformance of the proposed GuLiM method compared with the traditional direct mapping method. Moreover, a field investigation of GuLiM illustrates its potential for the teleoperation of medical assistive robots in the isolation ward as the Second Body of healthcare workers for telehealthcare, avoiding exposure of healthcare workers to the COVID-19.

Respiratory rate (RR) monitoring has received widespread attention in medical field. Complex, expensive, and bulky commercial equipment limits the monitoring cost. To take full advantage of lab-on-fiber technologies, we present a novel, compact size lab-on-fiber breathing monitoring sensor based on a tapered no-core fiber (NCF) structure. The sensor is composed of humidity-sensitive polymer materials, single-mode fiber (SMF)–NCF-SMF structure, and monitors human RR by measuring exhaled humidity. The theoretical results prove that the self-imaging phenomenon can be induced by the changing of NCF diameter. The static relative humidity test results show a maximum relative humidity sensitivity of 0.0438 nm/%RH in the range of 35%RH–58%RH. Through the single-wavelength intensity fluctuation experiment, human respiratory information is collected. In the breathing process, a response time of 0.67 s can be achieved. The RR monitoring under different heart rates, breathing patterns, and human postures displays real-time tracking and high repeatability. Moreover, the compact size, low cost, and high sensitivity make our lab-on-fiber sensor more competitive in the field of medical treatment and our daily life.

An integrated all-fiber sensor based on ring-core fiber (RCF)-embedded long period fiber grating (LPFG) cascaded RCF-Mach-Zehnder interferometer (MZI) for measuring curvature and temperature is proposed and demonstrated experimentally. The RCF-embedded LPFG section is fabricated by periodic embedding hundreds of micrometers sized RCF into SMF by employing the mature, low-cost cleaving splicing method (CSM). Due to the large core diameter mismatch, the energy within the core to be exceeded into the cladding mode more effectively, and the periodic refractive index modulation makes our sensor has a compact size with only 3-period SMF-RCF structures and a total length of 2.8 mm. To measure curvature and temperature simultaneously, a RCF-based MZI with the RCF length of 1 mm is cascaded behind LPFG by employing the CSM. Thus, there are two dips corresponding to LPFG and MZI occur in the transmission spectrum. The dips show different response to curvature and temperature changes. The maximum curvature and temperature sensitivities of the two dips are 26.61 nm/m ^-1 , -1.67 nm/m ^-1 , 0.054 nm/°C, and 0.057 nm/°C, respectively. By utilizing a 2×2 matrix, simultaneous measurement of curvature and temperature can be achieved. The integrated all-fiber sensor has a compact size, low cost,and high sensitivity possessing more competitivity in complex, harsh applications.

Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks. This method allows for the precise localization of leak points. The stability is compromised because of the wireless signal strength. The latter approach, which relies on pressure measurements to predict leak events, does not achieve precise leak point localization. To address these challenges, in this paper, a coherent optical time-domain reflectometry (φ-OTDR) system is employed to capture vibration signal phase information. Subsequently, two pre-trained neural network models based on CNN and Resnet18 are responsible for processing this information to accurately identify vibration events. In an experimental setup simulating water pipelines, phase information from both leaking and non-leaking pipe segments is collected. Using this dataset, classical CNN and ResNet18 models are trained, achieving accuracy rates of 99.7% and 99.5%, respectively. The multi-leakage point experiment results indicate that the Resnet18 model has better generalization compared to the CNN model. The proposed solution enables long-distance water-pipeline precise leak point localization and accurate vibration event identification.

A high-sensitivity curvature sensor with dual-parameter measurement ability based on angularly cascaded long-period fiber grating (AC-LPFG) is proposed and experimentally demonstrated, which consists of two titled LPFGs (TLPFGs) with different tilt angles and the same grating period. AC-LPFG was fabricated by using a deep ultraviolet laser and an amplitude-mask in our laboratory. The experimental results show that simultaneous measurement of curvature and temperature can be achieved by monitoring the wavelengths of two resonant peaks for different TLPFGs. The two peaks show opposite shifts with increasing curvature and has a maximum curvature sensitivity of 16.392 nm/m⁻¹. With the advantages of low cost, high sensitivity, and dual-parameter measurements, our sensor has more potential for engineering applications.