Objective Sleep monitoring is critical for early disease warning and personalized health management as respiratory rate, heart rate and body movement during sleep directly reflect physiological and pathological states. Traditional monitoring methods, such as electrocardiographs (ECGs) and polysomnographs (PSGs), are invasive, requiring electrode patches to be attached to the human body. Despite their high precision, they are poor in comfort, and tend to disrupt natural sleep. In contrast, non-invasive optical fiber sensors possess advantages including small size, electromagnetic interference (EMI) resistance, and high sensitivity, which enable them to perceive physical quantities such as vibration and pressure via wavelength or light intensity modulation, thus exhibiting significant potential in sleep monitoring. Nevertheless, noise interference persists during the processes of photoelectric conversion and signal acquisition, which impairs the detection accuracy of respiratory and cardiac signals. Existing denoising algorithms like wavelet soft thresholding have constant amplitude deviations, and offline-dependent models fail to meet real-time needs. This study aims to develop an all-fiber multimodal sleep monitoring system for non-invasive, high-precision and real-time detection of sleep respiratory rate, heart rate and turnover state, providing a reliable technical solution for home sleep health monitoring.

Methods A sleep monitoring system based on an all-fiber multi-modal sensing network was constructed. A 40 cm×30 cm sandwich structured flexible monitoring pad was fabricated, embedded with macrobend 1060XP single-mode fiber and dual fiber Bragg gratings. The macrobend fiber was optimized to a 20 mm bending radius with nine loops through experiments, arranged in two rows of semi-circular curves, embedded in a latex pad’s middle layer with silicone sheet covers to enhance cardiopulmonary vibration sensitivity and structural durability. Dual FBGs with initial wavelengths of 1548.625 nm and 1548.473 nm are symmetrically spaced 20 cm apart, bonded to a rigid PVC substrate using epoxy AB glue and fixed on the pad to offset temperature drift and capture turnover-induced strain. For signal processing, an improved threshold λ was proposed to achieve adaptive thresholding across different decomposition layers, considering the impacts of decomposition levels j and high-frequency wavelet coefficients cdn on threshold selection. Compensation term decay rate parameter α and exponential term growth rate β were introduced to optimize the threshold function. These two parameters dynamically regulate the output value of the threshold function when λ is applied, ensuring continuous and differentiable processing of cardiopulmonary signals while balancing noise suppression and weak signal preservation. Subsequently, second-order Butterworth band-pass filters are configured for respiratory and heartbeat signals, with the respiratory band 0.133–0.417 Hz and the heartbeat band 0.833–1.67 Hz to separate mixed signals, and fast Fourier Transform FFT is used to extract characteristic frequencies. After that, a body movement determination threshold was constructed based on the wavelength difference change rate of dual FBGs to distinguish left and right turns and count turn events. Experimental validation was conducted in a constant-temperature laboratory with a 61 kg subject lying supine on the monitoring pad and E-HA03 commercial electrocardiograph data was served as reference.

Results and Discussions Practical tests showed the improved denoising algorithm achieved a signal-to-noise ratio of 24.02 dB and a root mean square error of 0.09, outperforming traditional hard threshold method with signal-to-noise ratio of 14.32 dB and root mean square error of 0.27 as well as soft threshold method with signal-to-noise ratio of 7.12 dB and root mean square error of 0.62. Respiratory spectrum peaked at 0.33 Hz corresponding to 20 breaths per minute, heartbeat at 0.97 Hz, corresponding to 58 beats per minute. Compared with E-HA03 data 19 breaths per minute and 61 beats per minute, Respiratory rate error was 1 breath per minute and heart rate error was 3 bpm, meeting clinical home monitoring requirements. A turnover determination model was established based on the wavelength difference change rate of dual FBGs, where the left turnover threshold was set to ≥2.8 nm and the right turnover threshold to ≤−2.4 nm for distinguishing turnover directions and counting events. During the test, three left and three right turnovers were detected at 493, 530, 568, 597, 791, and 820 s, with no false or missed detections despite minor limb movements. The dual FBG design effectively offset temperature drift, with approximately stable wavelength baselines after thermal equilibrium. These results confirm the algorithm’s robustness and reliability in sleep scenarios.

Conclusions The non-invasive flexible pad ensured user comfort without disrupting sleep, and three key advancements are achieved: 1) the integration of macrobend fibers (optimized for bending radius and layout) and symmetrically arranged dual FBGs breaks the constraints of single-sensor systems, where the fibers capture subtle cardiopulmonary vibrations and the FBGs realize precise body-turnover detection to enable synchronous multimodal sensing of cardiopulmonary signals and body-turnover states. 2) The dual-parameter adaptive wavelet threshold denoising algorithm balances noise suppression and weak signal preservation, successfully capturing faint heartbeat signals that are easily masked by respiratory signals. 3) Symmetric dual FBGs combined with wavelength difference analysis offset temperature drift, enhancing the system’s stability in complex home environments. The all-fiber multimodal sleep monitoring system achieves high-precision non-invasive monitoring of respiratory rate, heart rate and turnover state. Its relatively excellent denoising performance, efficient signal separation and accurate turnover recognition provide a new technical approach for home sleep health monitoring, with broad application prospects in elderly care and personalized health management. Future work will optimize sensor array density to distinguish more turnover-related movements such as limb movements and expand clinical trials to diverse populations to enhance generalizability.