Twin-core fiber (TCF)-based sensor was proposed for non-invasive vital sign monitoring, including respiration and heartbeat. The TCF was homemade and the corresponding sensor was fabricated by sandwiching single-mode fiber (SMF) on both ends. The offset distance between SMF and TCF was optimized while the length of TCF was identified from preliminary vital sign measurement results. Then, the TCF-based sensor was attached under a mattress to realize non-invasive vital sign monitoring. Both respiration and heartbeat signal can be obtained simultaneously, which is consistent with the reference signals. For further application, post-exercise physiological activitity characterization were realized based on this vital sign monitoring system. In discussion, mode coupling in TCF was analyzed and utilized for curvature sensing with achieved sensitivity as high as 18 nm/m⁻¹, which supported its excellent performance for vital signs monitoring. In conclusion, the TCF-based vital signs monitors can be a promising candidate for healthcare and biomedical applications.

As a vital sign of human, ballistocardiography (BCG) signal is widely used to indicate the health status of an individual. It can be captured by optical fiber sensor in a non-invasive way. Besides, some significant physiological information, such as heart rate (HR) and respiration rate (RR), can also be extracted from BCG signal. As a medical instrument characterized by low power consumption, no interference, no invasion and the capability of real-time health monitoring, optical fiber sensor provides an effective solution to health care monitoring. However, when long-term monitoring of HR and RR is required for patients, it is necessary to collect a large amount of BCG data, which incurs a considerable amount of time and labor costs. To solve this problem, a novel deep learning model is proposed in this paper, namely ELA. Long-short term memory (LSTM) has the capability to memorize long-distance information states. According to this method, the improved empirical mode decomposition (EMD) algorithm (VEMD) is applied to convert the complex problem of HR and RR time series prediction into multiple sub-problems with relatively simple intrinsic mode function (IMF) component sequence prediction. Then, the proposed ELA model is adopted to model the HR and RR sub-sequences decomposed by VEMD. Accordingly, the results of IMF component subsequence prediction are obtained, with all the prediction results linearly summed to obtain the final prediction result. Moreover, experimental results are obtained to demonstrate the superiority of ELA and the R ² is 0.9946, indicating the excellent performance of the proposed model in the assessment and prediction of vital signs.

Cardiovascular diseases (CVDs) are very common in modern society, such as atherosclerosis, hypertension, etc., which have a great impact on heart function. Therefore, hospitalization monitoring alone is far from enough. Long-term monitoring of the heart is needed in daily life. The technology of non-invasive monitoring of the cardiovascular system can meet the needs of long-term monitoring of the heart condition, helping to promote the improvement of lifestyle and daily care, reducing the overall risk of developing CVDs. The purpose of this study is to investigate the cardiac response after different exercises using a $3\times 3$ demodulation scheme-based ballistocardiography (BCG) monitoring system. A fiber optic sensor (FOS)-based smart cushion is used to replace the traditional inconvenient electrocardiogram (ECG) for heart rate variability (HRV). The correlation between BCG inter-beat interval (IBI) and ECG IBI is 0.9862, and the RMSE is 0.0139. The BCG signal can assess cardiac contractility by analyzing RJ interval with ECG, which is a practical alternative to the pre-ejection period (PEP).

Due to the improvement of living standards, people’s attention to health has gradually increased. More and more people are willing to spend money and time on health management. This article reviews work on the vital signs monitoring system based on fiber optic interferometers, including the design of sensor structures, signal demodulation methods and data analysis. After a large number of trials, the system can achieve long-term stable heart rate (HR), respiration rate (RR) and body temperature monitoring, and the collected data can be used for health analysis. Due to the high sensitivity, low cost, and light weight of the interferometric fiber optic sensor, it can be integrated under a mattress or a cushion, which is very suitable for daily use. The system has great application prospects in the field of healthcare.

Continuous monitoring of nocturnal blood pressure is crucial for hypertension management and cardiovascular risk assessment. However, current clinical methods are invasive and discomforting, posing challenges. These traditional techniques often disrupt sleep, impacting patient compliance and measurement accuracy. Here we introduce a non-contact system for continuous monitoring of nocturnal blood pressure, utilizing ballistocardiogram signals. The key component of this system is the utilization of advanced, flexible fiber optic sensors designed to capture medical-grade ballistocardiogram signals accurately. Our artificial intelligence model extracts deep learning and fiducial features with physical meanings and implements an efficient, lightweight personalization scheme on the edge device. Furthermore, the system incorporates a crucial algorithm to automatically detect the user’s sleeping posture, ensuring accurate measurement of nocturnal blood pressure. The model underwent rigorous evaluation using open-source and self-collected datasets comprising 158 subjects, demonstrating its effectiveness across various blood pressure ranges, demographic groups, and sleep states. This innovative system, suitable for real-world unconstrained sleeping scenarios, allows for enhanced hypertension screening and management and provides new insights for clinical research into cardiovascular complications.

An all-optical active homodyne detection aided fiber-optic Mach-Zehnder interferometer (MZI) vital signs monitoring system is proposed and investigated. The active homodyne detection is mainly realized by employing a wavelength-tunable vertical-cavity surface-emitting laser (VCSEL) and automatic closed-loop control system (CLCS). It can effectively maintain the MZI sensor to operate around the quadrature point and successfully eliminate the fading effect of output signal induced by low frequency drift. Therefore, high fidelity and high stableness of the ballistocardiograph (BCG) signal is guaranteed. The results show that according to the Bland-Altman and the confidence ellipse analysis, the heart rate (HR) monitoring performance reveals high accuracy and good consistence. What’ more, a multiple linear regression-based blood pressure (BP) estimation model is presented, which shows that the BP is highly correlated with I-J-K complex oriented features, including IJ interval, JK interval and JK amplitude. It indeed offers a potential approach to achieve cuff-less BP estimation merely based on the high-fidelity BCG signal. In conclusion, the proposed MZI-BCG monitoring system is simple to use, and it shows great potential in future unobtrusive cardiac monitoring both in home and hospital.