Continuous noninvasive measurement of vital bio-signs, such as cardiovascular parameters, is an important tool in evaluation of the patient’s physiological condition and health monitoring. Based on new enabling technologies, continuous monitoring of heart and respiration rate, pulse wave velocity and blood pressure have been investigated, advanced and reflected in numerous papers published in recent years. In this paper, we introduce a new technique for noninvasive sensing of vital bio-signs based on a multimode optical fiber sensor that can be integrated into a fabric. The sensor consists of a laser, optical fiber, video camera and computer. Its operation is based on tracking of point-wise intensity variations on speckle patterns caused by interference of the light modes within the fiber subjected to deformation. The paper contains theoretical analysis and experimental validation of the proposed scheme. The main goal is to advance a simple low-cost sensor embedded in a cloth fabric to track changes in the cardiovascular condition of the wearer.

. Significance: The ability to perform frequent non-invasive monitoring of glucose in the bloodstream is very applicable for diabetic patients. Aim: We experimentally verified a non-invasive multimode fiber-based technique for sensing glucose concentration in the bloodstream by extracting and analyzing the collected speckle patterns. Approach: The proposed sensor consists of a laser source, digital camera, computer, multimode fiber, and alternating current (AC) generated magnetic field source. The experiments were performed using a covered (with cladding and jacket) and uncovered (without cladding and jacket) multimode fiber touching the skin under a magnetic field and without it. The subject’s finger was placed on a fiber to detect the glucose concentration. The method tracks variations in the speckle patterns due to light interaction with the bloodstream affected by blood glucose. Results: The uncovered fiber placed above the finger under the AC magnetic field (150 G) at 140 Hz was found to have a lock-in amplification role, improving the glucose detection precision. The application of the machine learning algorithms in preprocessed speckle pattern data increase glucose measurement accuracy. Classification of the speckle patterns for uncovered fiber under the AC magnetic field allowed for detection of the blood glucose with high accuracy for all tested subjects compared with other tested configurations. Conclusions: The proposed technique was theoretically analyzed and experimentally validated in this work. The results were verified by the traditional finger-prick method, which was also used for classification as a conventional reference marker of blood glucose levels. The main goal of the proposed technique was to develop a non-invasive, low-cost blood glucose sensor for easy use by humans.

Speckle pattern analysis has been found by many researchers to be applicable to remote sensing of various biomedical parameters. This paper shows how analysis of dynamic differential speckle patterns scattered from subjects’ sclera illuminated by a laser beam allows extraction of micro-saccades movement in the human eye. Analysis of micro-saccades movement using advanced machine learning techniques based on convolutional neural networks offers a novel approach for non-contact assessment of human blood oxygen saturation level (SpO2). Early stages of hypoxia can rapidly progress into pneumonia and death, and lives can be saved by advance remote detection of reduced blood oxygen saturation.

Continuous noninvasive measurement of intraocular pressure (IOP) is an important tool in the evaluation process for glaucoma. We present a methodology enabling high-precision, noncontact, reproducible, and continuous monitoring of IOP based on the value of the damping factor of transitional oscillations obtained at the surface of the eye after terminating its stimulation by a sound wave. The proposed configuration includes projection of a laser beam and usage of a fast camera for analyzing the temporal-spatial variations of the speckle patterns backscattered from the iris or the sclera following the above-mentioned sound waves external stimulation. The methodology was tested on an artificial eye and a carp fish eye under varying pressure as well as on human eyes.

Neural activity research has recently gained significant attention due to its association with sensory information and behavior control. However, the current methods of brain activity sensing require expensive equipment and physical contact with the tested subject. We propose a novel photonic-based method for remote detection of human senses. Physiological processes associated with hemodynamic activity due to activation of the cerebral cortex affected by different senses have been detected by remote monitoring of nano‐vibrations generated by the transient blood flow to the specific regions of the human brain. We have found that a combination of defocused, self‐interference random speckle patterns with a spatiotemporal analysis, using Deep Neural Network, allows associating between the activated sense and the seemingly random speckle patterns.