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Fiber-integrated fabric for non-tight contact bio-sensing of vital signs

机译:纤维集成织物,用于生命体征的非紧密接触生物传感

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In this research we present a novel configuration allowing to perform high precision sensing of various vital bio-signs obtained from a fiber-based sensor performing the measurements in a non-tight contact with the skin of the measured subject. We will discuss usage of various types of fibers: single as well as multi-mode. Laser beam is injected into the fiber. In the case of a single mode fiber along the fiber, special artifacts that are breaking the total internal reflection condition, are inserted. Those artifacts are causing to some portion of the injected light to escape the fiber and to interact with the nearby surrounding of the fiber, realizing a smart photonic drip. Changes in the resulted interference-based intensity at the output of the fiber-sensor is analyzed and associated with various bio-medical signs. In the case of a multi-mode fiber, a detector analyzes the temporal-spatial changes of the 2-D speckle pattern imaged at the tip of the fiber. In both cases the fiber strain, temperature change and vibration associated movements occurring in the proximity of the fiber or in the fiber itself, cause change of the fiber propagating photons phase, polarization and amplitude which leads to temporal-spatial changes in the analyzed speckle pattern or in the resulted interference based intensity measured at the output of the fiber-sensor. After applying proper artificial intelligence (AI) algorithmic, one may correlate those small changes with various vital bio-signs such as heart rate, heart rate variability (HRV), heart sound (phono-cardiogram), respiration rate and sound and even blood pressure.
机译:在这项研究中,我们提出了一种新颖的配置,可以对从基于纤维的传感器以与被测对象的皮肤进行不紧密接触的方式进行测量而获得的各种重要生物信号进行高精度感测。我们将讨论各种类型的光纤的用法:单模和多模。激光束被注入到光纤中。对于沿光纤的单模光纤,将插入破坏整个内部反射条件的特殊伪像。这些伪像使注入的光的一部分逸出光纤,并与光纤附近的周围环境相互作用,从而实现了智能的光子滴注。分析了光纤传感器输出端基于干涉的强度变化,并将其与各种生物医学信号相关联。在多模光纤的情况下,检测器分析在光纤尖端成像的二维斑点图案的时空变化。在这两种情况下,在光纤附近或光纤本身中发生的光纤应变,温度变化和与振动相关的运动都会导致光纤传播的光子相位,偏振和振幅发生变化,从而导致所分析的散斑图样随时间发生变化或在光纤传感器的输出端测得的基于干扰的强度。应用适当的人工智能(AI)算法后,可以将这些细微变化与各种重要的生物体征相关联,例如心率,心率变异性(HRV),心音(心电图),呼吸频率以及声音甚至血压。

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