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Diabetes may result in the development of secondary complications that can be life-threatening, such as cardiovascular disease and renal failure. Other less severe secondary complications related to diabetes include nerve damage, ketosis, and various skin conditions. All the above complications dramatically affect the quality of life of patients who have diabetes. Accurately being able to measure blood glucose is an essential step in the healthcare of diabetes patients.




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Traditional glucometers fall within a family of devices that utilize enzymatic reactions to produce electrical signals readable using the meter. These enzymatic reactions are glucose oxidase, glucose dehydrogenase, and hexokinase. The hexokinase method is the de facto gold standard for its high specificity [3]. Practically, this home-monitoring (e.g., self-monitoring) approach, which requires a drop of blood, leads to poor monitoring habits, resulting in few measurements per day (i.e., three to seven samples per day), and providing a brief glimpse of blood glucose. Several methods have been proposed to improve the sampling rate of traditional self-monitoring techniques and overcome their inconveniences.


where SSres is the sum of squares of the residuals (also known as errors), and SStot is the total sum of squares, which is proportional to the variance of the target data. In Equation (2), y refers to the mean of the target glucose concentration. Generally, the R2 value ranges between 0 and 1. If the R2 value is less than zero, this means the model is arbitrarily performing worse than if it was guessing the mean of the target variable. An R2 value equal to zero would indicate that the predictions are no better than if the regression algorithm assumed the mean of the target for every prediction. If the R2 value is equal to one, the model accurately predicts each target value without errors (e.g., maximum accuracy).


These datasets were passed through the machine learning pipeline (described in Section 2.3). Each model was trained using 10-fold cross-validation. Figure 4 shows the results of the best model (e.g., lower RMSE value). For both subjects, the best model was an ensemble method called Bagged Trees. From Figure 4, we can conclude that the predicted values (orange circles in panels (a) and (c)) do not follow the same trend as the target glucose concentrations (blue circles in panels (a) and (c)). These results indicate that the four features from the Ohio dataset do not have sufficient predictive power to estimate blood glucose levels. The mean and standard deviation lines are also plotted in Figure 4a,c to highlight the low performance of the models. We observe that the models tend to choose the safest predictions around one standard deviation from the mean of the target/reference data, which provides the minimum RMSE value, rather than accurately predicting the target output. Note that no predictions were made outside one standard deviation of the average.


Performance of the best-trained model for both subjects (a,b for Subject 559 and c,d for Subject 563) from the OhioT1DM dataset. In panels (a,c), the predicted and target glucose concentrations are sorted in ascending order. The Clarke Error Grids (reference versus predicted glucose values) are shown in panels (b,d). The Clarke Error Grid separates the measurements into five regions based on their accuracy; read Section 2.4 for more details.


The correlation coefficient between target glucose concentrations and the selected fourteen features for both subjects, (a) Subject 1 and (b) Subject 2, from the UofM dataset.


Performance of the best-trained model in unseen instances for both subjects from the UofM dataset. Panels (a,c) show the predicted and target glucose concentrations from unseen data. The Clarke Error Grids are shown in panels (b,d). RMSE and R2 values of the best model are reported in panels (b,d). We also report the percentage of instances (red font) falling in each region of the Clarke Error Grid.


Future work will also be focused on investigating more sophisticated methods to preprocess the dataset. For example, in this study, the dataset was generated by simply removing outliers, averaging the values over the same time signature, and selecting the values that match with the timestamp of the target blood glucose. We will also consider extracting additional features from current measurements to improve the predictive model in future work. For example, we could use the MATLAB-based KARDIA software package to measure phasic cardiac responses and time- and frequency-domain heart rate variability using the IBI data generated by the Empatica E4 wristband [20]. Note that blood glucose levels have been well correlated to heart rate variability using photoplethysmography [19,21,22]. In addition, we could investigate the extraction of better features by decomposing the skin conductance (i.e., EDA) into its tonic and phase components using the MATLAB-based software package Ledalab [40]. These two packages are distributed free of charge.


Future work should also be focused on investigating a global model (as opposed to user-specific or local), including specific user information as input features. This would allow a global model to discriminate based on key biometrics (such as BMI, age, race, and gender) to increase the accuracy of blood glucose level predictions for individuals.


B.B.-J. thanks Haley Fong for assisting and providing an extra hand during data collection of the UofM dataset. A.D. acknowledges the help of Sara Armstrong from the Technology Transfer Office from Ohio University and Margie Robertson from the University of Memphis for providing the OhioT1DM dataset. A.D. would like to thank Josu Feijoo for the discussion about the need of noninvasive sensors to predict blood glucose levels for people who have diabetes.


Your watch will help you monitor your glucose even when you are busy with life. It will alert you to highs or lows with discreet vibrations. You will be able to program different alert levels for night time and even send alerts to your family or friends*.


Such monitors use a catheter that needs to be inserted under the skin every two weeks or so. They also suffer from other drawbacks such as a lag time in glucose measurements, said Irl Hirsch, a physician and professor at University of Washington Medicine Diabetes Institute.


For its first years of operation, Know Labs focused on light-based authentication and diagnostic technology. Then called Visualant, the company changed its name and focused on glucose monitoring in 2018. It also brought on a new CEO, Phil Bosua, whose previous roles include vice president of consumer products at lighting technology startup Soraa, and CEO and founder of smart lightbulb company LIFX.


At the big Consumer Electronics Show (CES) in early January 2022, an artificial intelligence company based in British Columbia named Scanbo gave a glimpse of its technology that would use a 60-second noninvasive finger measurement instead of a traditional blood drop required to measure glucose. The company has developed a prototype that combines a 3-lead ECG measurement and a Photoplethysmogram (PPG) used to detect blood volume. You just put your fingers on the flat white sensors and the system uses a set of algorithms to analyze and offer insight on glucose values.


Seattle, Washington-based Know Labs is developing two devices that employ Body-Radio Frequency Identification (Bio-RFID) technology, which uses radio waves to measure specific molecular signatures in the blood through the skin. Formerly known as Visualant, this tech company changed its name in 2018 and is developing both a wristband-style device as well as a finger-scanning device that eliminate the need to pierce the skin to get glucose readings.


Created by startup co-founder Dr. Werner Mäntele, this technology has shown in research from 2020 that it has comparable accuracy to the minimally invasive FreeStyle Libre Flash glucose monitor from Abbott Diabetes.


The NovioSense device consists of a flexible metal coil just 2 centimeters long that contains nanosensors inside. The coil is covered by a protective layer of soft hydrogel, and it could measure constant changes in glucose levels from tear fluid using the same enzyme technology employed in conventional glucose test strips.


This Silicon Valley, California-based startup is developing a noninvasive wearable wristwatch called LifeLeaf. The company says it can detect blood glucose levels, blood pressure, heart rate, sleep apnea, and more by using sensors already on the market and an additional light sensor to enhance accuracy.


According to a January 2021 report, Apple may be working on their own glucose monitoring tech that would use an integrated optical glucose sensor. The report has some fascinating visuals on what the Apple Watch display could look like.


There had been talk years back about a Samsung and Medtronic Diabetes partnership aimed at integrating glucose data into Android watches, but that relationship faded without any product materializing beyond prototypes.


Another notable name in noninvasive CGM tech for several years was C-8 MediSensors based in San Jose, California. This gadget promised to use light to identify and analyze glucose molecules under the skin via interstitial fluid, just like other traditional CGMs.


This company even obtained European CE Mark approval in 2012, but a launch never materialized and, eventually, the company went bankrupt a year later. Many of the C-8 scientists moved on to other companies like Apple and Google, before the company eventually rebranded and relaunched as C-Eight without any focus on noninvasive glucose monitoring.


A lot of problems arise when a human cannot control the insulin level and thus process the glucose concentration in the blood. This inability initiates diabetes [1], which is a disease where the blood glucose level is high. In this case, only a precise therapy and careful management can prevent a buildup of sugars in the blood and intolerance to glucose [2], increasing the risk of dangerous vascular complications [3], such as coronary artery disease (leading to heart attack) [4], peripheral vascular disease, kidney failure or stroke, and neural complications (diabetic neuropathy) [5], including peripheral neuropathy and autonomic nervous system failure. 041b061a72


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