The microcirculatory blood flow can be assessed using laser-based techniques, which indirectly quantify the Doppler shifts that occur when light is scattered by moving red blood cells. These techniques are commonly based on either a temporal^1,2 or spatial^3,4 analysis of the dynamic speckle pattern formed when Doppler shifted and nonshifted light is backscattered from a laser illuminated tissue and mixed on a detector. In laser Doppler flowmetry (LDF), an almost direct mapping of Doppler shifts can be attained by studying the frequency distribution of the temporal speckle fluctuations. This technique can produce perfusion estimations that are proportional to the blood flow speed if the Nyquist criterion for the photodetector current is fulfilled. Sampling rates well above 10 kHz are typically required, which has limited the LDF applicability to mainly include single point instruments capable of studying variations in tissue perfusion over time, or slow scanning systems measuring one point after the other. Laser speckle contrast imaging (LSCI) presents one way of overcoming the single point limitation while keeping a high time-resolution. The LSCI technique is based on a spatial analysis of the speckle dynamics, where Doppler effects are indirectly quantified as local blurring caused by speckle motion. The speckle blurring effect, measured as the local image contrast, depends on the exposure time of the camera. While short exposure times can capture fast variations related to high Doppler shifts, longer exposure times can be related to the degree of Doppler shifted light.⁵ Compared to LDF, there is no direct relationship between the distribution of Doppler frequencies and the speckle contrast for single exposure time LSCI.

A multiple exposure laser speckle contrast imaging (MELSCI) setup for visualizing blood perfusion was developed using a field programmable gate array (FPGA), connected to a 1000 frames per second (fps) 1‐megapixel camera sensor. Multiple exposure time images at 1, 2, 4, 8, 16, 32 and 64 milliseconds were calculated by cumulative summation of 64 consecutive snapshot images. The local contrast was calculated for all exposure times using regions of 4 × 4 pixels. Averaging of multiple contrast images from the 64‐millisecond acquisition was done to improve the signal‐to‐noise ratio. The results show that with an effective implementation of the algorithm on an FPGA, contrast images at all exposure times can be calculated in only 28 milliseconds. The algorithm was applied to data recorded during a 5 minutes finger occlusion. Expected contrast changes were found during occlusion and the following hyperemia in the occluded finger, while unprovoked fingers showed constant contrast during the experiment. The developed setup is capable of massive data processing on an FPGA that enables processing of MELSCI data in 15.6 fps (1000/64 milliseconds). It also leads to improved frame rates, enhanced image quality and enables the calculation of improved microcirculatory perfusion estimates compared to single exposure time systems.

Objective. The objective of this paper is to present a driver sleepiness detection model based on electrophysiological data and a neural network consisting of convolutional neural networks and a long short-term memory architecture. Approach. The model was developed and evaluated on data from 12 different experiments with 269 drivers and 1187 driving sessions during daytime (low sleepiness condition) and night-time (high sleepiness condition), collected during naturalistic driving conditions on real roads in Sweden or in an advanced moving-base driving simulator. Electrooculographic and electroencephalographic time series data, split up in 16 634 2.5 min data segments was used as input to the deep neural network. This probably constitutes the largest labeled driver sleepiness dataset in the world. The model outputs a binary decision as alert (defined as ≤6 on the Karolinska Sleepiness Scale, KSS) or sleepy (KSS ≥ 8) or a regression output corresponding to KSS ϵ [1–5, 6, 7, 8, 9]. Main results. The subject-independent mean absolute error (MAE) was 0.78. Binary classification accuracy for the regression model was 82.6% as compared to 82.0% for a model that was trained specifically for the binary classification task. Data from the eyes were more informative than data from the brain. A combined input improved performance for some models, but the gain was very limited. Significance. Improved classification results were achieved with the regression model compared to the classification model. This suggests that the implicit order of the KSS ratings, i.e. the progression from alert to sleepy, provides important information for robust modelling of driver sleepiness, and that class labels should not simply be aggregated into an alert and a sleepy class. Furthermore, the model consistently showed better results than a model trained on manually extracted features based on expert knowledge, indicating that the model can detect sleepiness that is not covered by traditional algorithms.

Noninvasive in vivo imaging of microcirculatory blood flow or perfusion (average speed times concentration of moving red blood cells) is of interest in several clinical applications, including monitoring of burn wounds^1,2 and investigation of peripheral arterial disease,³ as well as several others.⁴ One technique that has gained a lot of focus in the last decades is laser speckle contrast imaging [LSCI; sometimes laser speckle contrast analysis (LASCA)] in which tissue is illuminated with a laser and the resulting speckle pattern is detected with a camera. Light that scatters when interacting with moving particles in the tissue will obtain a Doppler frequency shift depending on the velocity of the particles. The interference of light with different frequencies will give rise to fluctuations in the speckle pattern formed on the imaging sensor. The movement of the speckles on the sensor will cause an image blur that increases with exposure time. The local spatial statistics of this blur can be related to the movement of particles in the tissue, as is done in LSCI.⁵

Laser speckle contrast imaging (LSCI) is a noninvasive measurement technique for assessing spatial perfusion maps of the microcirculation.¹ When coherent laser light scatters from moving red blood cells (RBCs) in tissue, it changes frequency due to the Doppler effect. Backscattered light from the tissue will therefore create a fluctuating speckle interference pattern on the camera sensor. These fluctuations cause motion blur in the recorded image, which can be quantified as the local contrast in a region of pixels. Several mathematical models have been proposed to establish a relation between the speckle contrast and blood flow speed or perfusion (speed × concentration). However, the amount of Doppler shifted light and the distribution of Doppler frequencies can affect a single-exposure contrast value in similar ways. This contributes to limit LSCI to relative estimates of perfusion in arbitrary units. It has been demonstrated that perfusion by LSCI follows a nonlinear and unknown relation to perfusion measured by laser Doppler flowmetry (LDF)^{2 – 5} and to actual tissue perfusion.⁶