A Nearable Soft Mat Based on Distributed Optical Fiber Sensing for Physiological Monitoring

IF 4.5 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Sensors Journal Pub Date : 2026-10-15 Epub Date: 2026-09-01 DOI:10.1109/JSEN.2026.3727664
V. Lavorgna;M. Pulcinelli;A. Polimadei;R. D'Amato;C. Massaroni;M. A. Caponero;E. Schena;D. Lo Presti
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Abstract

Distributed optical fiber sensing (DOFS) combines the advantages of fiber-optic sensors (FOSs), including flexibility, small size, immunity to electromagnetic interference, and high metrological performance, with the capability to transform a single optical fiber into a continuous sensing element for spatially resolved mechanical measurements. Optical frequency domain reflectometry (OFDR), based on Rayleigh backscattering, enables high-spatial-resolution DOFS measurements, broadening the range of potential sensing applications. However, OFDR-based DOFS remains largely unexplored for biomedical applications, despite the need for sensitive, spatially resolved, and conformable sensing interfaces. This study presents a soft DOFS-based mat (DM) as a large-area interface for physiological monitoring. A single-mode optical fiber was embedded in a flexible silicone matrix and arranged in a serpentine layout to distribute sensing over the mat surface. With a gauge pitch of 2.6 mm, the system provided 2250 sensing sites across the active area at a sampling frequency of 50 Hz. The mat was assessed on six healthy volunteers in a seated nearable configuration on the backrest of a standard office chair. The distributed output enabled 2-D mapping of the mat response, reflecting back–mat mechanical coupling and cardiorespiratory-induced perturbations. Respiratory rate (RR) and heart rate (HR) were therefore estimated and compared with a reference wearable system. The maps revealed physiologically coherent spatial and temporal patterns, while the estimated rates showed good agreement with the reference measurements. These results demonstrate the feasibility of combining large-area distributed sensing, spatial mapping, and quantitative cardiorespiratory monitoring within a DOFS-based soft nearable interface.
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基于分布式光纤传感的近距离软垫生理监测
分布式光纤传感(DOFS)结合了光纤传感器(FOSs)的优点,包括灵活性,小尺寸,抗电磁干扰和高计量性能,以及将单个光纤转换为连续传感元件的能力,用于空间分辨机械测量。基于瑞利后向散射的光频域反射(OFDR)技术能够实现高空间分辨率的DOFS测量,扩大了潜在传感应用的范围。然而,基于ofdr的DOFS在生物医学应用中仍未得到充分开发,尽管需要敏感、空间分辨和兼容的传感接口。本研究提出了一种基于软dofs的垫子(DM)作为生理监测的大面积接口。将单模光纤嵌入柔性硅基中,并以蛇形布局布置,使传感分布在垫子表面。该系统的测量距为2.6 mm,以50 Hz的采样频率在活动区域内提供2250个传感点。六名健康志愿者坐在标准办公椅的靠背上,对这种垫子进行了评估。分布式输出实现了垫响应的二维映射,反映了垫后机械耦合和心肺诱导的扰动。因此,估计呼吸速率(RR)和心率(HR),并与参考可穿戴系统进行比较。这些地图揭示了生理上连贯的空间和时间模式,而估计的速率与参考测量结果显示出良好的一致性。这些结果证明了在基于dofs的软近距离接口中结合大面积分布式传感、空间映射和定量心肺监测的可行性。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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