Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals.

Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate-the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity.

Correction of overexposure in laser speckle contrast imaging.

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.

Ensemble averaging laser speckle contrast imaging: statistical model of improvement as function of static scatterers.

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.

Time varied illumination laser speckle contrast imaging.

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.