A conventional distributed fiber optic sensing system offers close to linear sensitivity along the fiber length. However gold nanoparticles (NP) have been shown to be able to enhance the contrast ratio to improve the quality of signal detection. The challenge in improving the contrast of reflected signals is to optimise the nanoparticle doping concentration over the densed sensing length to make best use of the distributed fiber sensing hardware. In this paper, light enhancement by spherical gold NPs in the optical fibers was analyzed by considering the size-induced NP refractive index changes. This was achieved by building a new model to relate backscattered light from a gold NP suspension between the optical fiber end tips and backscattered light from gold NPs in the core of the optical fiber. The paper provides a model to determine the optimized sizes and concentrations of NPs for sensing at different desired penetration depths in the optical fiber.

Nanoparticle (NP) doping of optical fibres can be used to increase the intensity of the backscattered light used for distributed strain sensing and has shown the advantages of high precision strain detection and multiplex sensing experimentally. However, the backscatter spectral characteristics of NP-doped optical fibres have not been described even though they are quite different from the spectra from fibre Bragg gratings (FBGs) or commercial single mode fibres. In this paper, gold NPs, used as the contrast agent in the optical fibre to increase the intensity of the backscattered light, were investigated from the aspect of their spectra. A single scattering model with Mie theory and an effective refractive index (RI) model were used to evaluate the backscattered light spectra and the Monte Carlo Method was used for seeding NPs. The results showed that the strain responsivity of gold-NP doped fibres with low volume ratio doping (single scattering restriction) are close to FBGs and commercial fibres. High volume ratios of gold NP doping increase the imaginary part of the RI of the optical fibre, which has a significant influence on the spectra in the wavenumber domain. These theoretical insights may promote the future engineering design of NP-doped fibre sensors.

Optical fiber sensors, known for their small size, lightweight, and resistance to electronic interference, are widely used in various applications, including medical vital signs monitoring and Internet of Medical Things (IoMT). They are particularly useful in recording electrocardiogram (ECG) signals and measuring heart rate variability (HRV), which provides insights into the autonomic nervous system activity. HRV, affected by sleep quality, can be used to assess sleep stages. Monitoring ECG and HRV during sleep can identify sleep disruptions and guide interventions to improve sleep quality. However, existing optical fiber sensors for vital signs monitoring have some drawbacks, including signal loss, sensitivity to light source intensity changes, signal distortion during long-distance transmission, and high manufacturing and maintenance costs. They also may produce missing or anomalous data due to sensor failures, transmission and storage issues, or other unforeseen factors. Conventional monitoring methods can be uncomfortable for long-term daily ECG and HRV monitoring. To address these problems, we propose a novel optical fiber sensor based on fiber interferometer and a robust semi-supervised framework, termed EBLA, which is composed of CEDANN and GSCM modules, can reconstruct and analyze raw ECG signals, extract temporal and spatial features, and match the relationship between HRV and sleep stages. The framework also applies external knowledge of acquired signal for graph modeling for the first time, revealing data relationships and better understanding the global sample structure from the raw ECG signal. The average root mean square error (RMSE) and mean absolute error (MAE) of experiments reach 1.711 and 1.196, respectively, demonstrating its feasibility and effectiveness, exhibiting a better effect and its superior to the state-of-the-art approaches. This work has the potential to promote the smart healthcare monitoring and application of optical fiber sensing.

Nanoparticle‐ (NP‐) doped optical fibres show the potential to increase the signal‐to‐noise ratio and thus the sensitivity of optical fibre strain detection for structural health monitoring. In this paper, our previous experimental/simulation study is extended to a design study for strain monitoring. 100 nm spherical gold NPs were randomly seeded in the optical fibre core to increase the intensity of backscattered light. Backscattered light spectra were obtained in different wavelength ranges around the infrared C‐band and for different gauge lengths. Spectral shift values were obtained by cross‐correlation of the spectra before and after strain change. The results showed that the strain accuracy has a positive correlation with the relative spectral sensitivity and that the strain precision decreases with increasing noise. Based on the simulated results, a formula for the sensitivity of the NP‐doped optical fibre sensor was obtained using an aerospace case study to provide realistic strain values. An improved method is proposed to increase the accuracy of strain detection based on increasing the relative spectral sensitivity, and the results showed that the error was reduced by about 50%, but at the expense of a reduced strain measurement range and more sensitivity to noise. These results contribute to the better application of NP‐doped optical fibres for strain monitoring.

Human vital signs are essential to the healthcare industry and applications of Internet of medical things (IoMT). As a significant vital sign signal, heart rate variability (HRV) provides into the functioning of the human stress levels and overall well-being. However, it is inconvenient for daily long-term HRV monitoring with conventional monitoring methods under the uncomfortable feelings. Moreover, it is hard to detect anomalies in HRV data to provide real-time previews about vital signs. To address these problems, a novel fiber interferometer-based optical fiber sensor is proposed to monitor human vital signs, and we propose a novel deep learning model (TWFN). First, a novel module (ADSN) is proposed to apply graph modeling for obtaining spatial and temporal characteristics of HRV. Subsequently, an unsupervised generative adversarial network (VS-GAN) is proposed for the effective overcoming mode collapse and generator failure to converge to the target distribution better. The outcomes of the experiment might encourage the use of HRV-based healthcare application in IoMT-enabled healthcare sectors.