Non-contact visual monitoring of vital signs in neonatology has been demonstrated by several recent studies in ideal scenarios where the baby is calm and there is no medical or parental intervention. Similar to contact monitoring methods (e.g., ECG, pulse oximeter) the camera-based solutions suffer from motion artifacts. Therefore, during care and the infants’ active periods, calculated values typically differ largely from the real ones. In this way, our main contribution to existing remote camera-based techniques is to detect and classify such situations with a high level of confidence. Our algorithms can not only evaluate quiet periods, but can also provide continuous monitoring. Altogether, our proposed algorithms can measure pulse rate, breathing rate, and to recognize situations such as medical intervention or very active subjects using only a single camera, while the system does not exceed the computational capabilities of average CPU-GPU-based hardware. The performance of the algorithms was evaluated on our database collected at the I^st Dept. of Neonatology of Pediatrics, Dept of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary.

The article describes a reference and training set free incrementally trained deep learning algorithm for camera-based respiration monitoring systems. The algorithm uses a model based discriminator to find salient areas having respiration like periodic motion. It stores the first principle component of the found waveforms into two slowly growing set along with negative, uncorrelated motion patterns. Using these samples, it trains a deep neural network classifier incrementally to recognize respiration from sudden and motion intensive situations. The classifier had no forgetting mechanism and it is able to adapt quickly the changing respiration patterns and conditions. The algorithm has been validated in a total of 24 hours diverse recording captured in the neonatal intensive care unit (NICU) of the Ist Dept. of Pediatrics and, II. Dept. of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary and in the COHFACE publicly available dataset of adult subjects. The clinical data set evaluation resulted in mean absolute error (MAE) 6.9 and root mean squared error (RMSE) of 9.8 breaths per minute, respectively, the MAE was below 5 breaths per minute for over 50% of the time. The algorithm was assessed in the COHFACE dataset of adult subjects as well with respiration estimation MAE and RMSE values of 0.95 and 1.7 breaths per minute.

Laser speckle contrast imaging is a technique to determine blood flow rate with a limitation of low dynamic range. In this Letter, we introduce a varied illumination speckle contrast imaging method. It utilizes varying illumination during exposure to customize the correlation time (flow rate) to speckle contrast relation. The method can cover an order of magnitude larger range flow rate in a single exposure compared to constant illumination methods. The proposed method enables high dynamic range flow rate imaging, which is advantageous in studying larger vessels and small arteries. We demonstrate the theory by simulations and ex vivo and in vivo measurements. (C) 2021 Optical Society of America

The appearance of the common artifacts of laser speckle contrast imaging (LSCI), namely the granularity in flow rate estimation caused by static scatterers, is a well-known phenomenon. This artifact can be greatly reduced in spatial speckle contrast calculation using interframe decorrelated illumination, forcing true ensemble averaging. We propose a statistical model, which describes the effect of multiple image acquisitions on the contrast map quality when the illumination stable and when the illumination is decorrelated frame by frame. We investigate the improvement as a function of the ratio of dynamic and static scatterers by formulating a statistical distribution based model, using in simulation, flow phantom and in vivo experiments. Our main finding is that the ensemble averaging yields limited improvement in several practical cases due to the highly heterogeneous scatterer structure of living tissues.

Laser speckle contrast imaging (LSCI) is a method to visualize and quantify tissue perfusion and blood flow. A common flaw in LSCI variants is their sensitivity to the optical setup parameters and that they operate well only on statistics of undistorted laser speckle patterns. The signal saturation of the sensors makes the contrast calculation misleading; hence the illumination level must be well controlled. We describe the theoretical explanation for the saturation-caused degradation. We introduce a linear extrapolation method to eliminate the overexposure induced error up to an extent of 60-70% saturated pixel count. This, depending on the contrast value and use case, enables to use 3-8 times higher external illumination level with no deterioration of the contrast calculation and thus the measured blood flow index. Our method enables a higher signal-to-noise ratio in darker areas by allowing the use of higher illumination, utilizing a larger portion of the dynamic range of the sensors, and making the illumination level setting less cumbersome.

A dual-pixel APS sensor architecture is proposed in this paper, for vision based speed measurement applications, based on a novel double exposure method. The sensor integrates two type of imaging elements on pixel level, and is designed to generate two spatially and temporally coherent images. The primary sensor generates a good quality image for vehicle identification, while the output of the secondary sensor is used to calculate speed estimates, based on the intra-frame displacement of the vehicle's headlight. A scaling process has also been developed for the sensitivity of the secondary sensor, based on photodiode parasitic capacitor discharge time