Integrated Multi-Modal Glucose Detection

Self-calibrating glucose continuous monitoring system that can accurately monitor blood glucose levels by extracting interstitial fluid from the skin and measuring glucose concentration in that fluid. The system uses a flexible film adhered to the skin with components like glucose sensors, sodium ion sensors, and fluid extraction electrodes. A differential sodium ion sensor measures the sodium concentration change in extracted fluid versus natural sweat to calculate the fluid volume. This volume correction improves accuracy compared to just measuring glucose concentration.

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Continuous glucose monitoring system that can accurately measure multiple analytes like glucose and oxygen in the body by periodically switching the sensor bias conditions. The system has an in vivo sensor with a working electrode and a reference electrode. It applies different bias conditions to the electrodes to measure glucose and oxygen concentrations separately. This allows differentiation between the analytes since their signals are affected differently by the bias conditions. By alternating the bias conditions, the sensor can provide separate measurements for glucose and oxygen levels.

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Determining glucose levels during continuous glucose monitoring (CGM) measurements using a CGM device that applies a constant voltage to the sensor and additionally probes the sensor with modulated potentials. The CGM device measures a primary current signal from the constant voltage and a modulated current signal from the probing potentials. It uses a transformation function to determine an initial glucose concentration from the primary signal, and a connection function to adjust the initial glucose to a final value based on the modulated signal. This allows accurate glucose determination during continuous sensing without calibration or finger sticks.

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System to augment analyte monitoring by leveraging a separate wearable device that can measure additional physiological signals. The primary analyte monitoring device collects analyte data like glucose. A separate wearable with additional sensors contacts the skin around the primary device and communicates its data. Both wearables send their data to a central hub. This allows correlation and augmentation of analyte measurements with other physiological signals.

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A device for measuring blood pressure that accommodates changes in applied pressure during a heartbeat by combining features from occlusion, pulse wave velocity (PWV), and pulse wave analysis (PWA) devices. The device measures arterial area changes during a heartbeat, applies pressure to the body part, and measures instantaneous applied pressure. It finds blood pressure by analyzing area vs. applied pressure. This compensates for pressure fluctuations during a heartbeat to make accurate measurements.

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Robust glucose monitoring system using orthogonally redundant sensors to improve accuracy and reliability compared to single sensor systems. The system has two independent sensors, one electrochemical and one optical, to measure glucose levels. The sensors are calibrated separately but cross-referenced using a mapped value. This allows accurate glucose readings even if one sensor fails or has errors due to environmental factors. The orthogonal redundancy provides true redundancy through distinct sensor technologies and failure modes.

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A method for accurately determining glucose levels in blood using a continuous glucose monitoring (CGM) system with a sensor in the interstitial tissue. The method involves modeling the blood-tissue-sensor glucose diffusion process and using a Kalman filter and smoother to estimate the current blood glucose from the tissue measurements. It improves accuracy by considering the time delay between blood and tissue glucose levels, adaptively estimating measurement noise, and checking for outliers. The filtering and smoothing allows more accurate estimation of blood glucose trends compared to just filtering the tissue measurements.

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A device for continuous glucose monitoring that combines electrochemical analysis and optical fiber sensing to provide more accurate and reliable measurements compared to just electrochemical analysis. The device has two parts: an electrochemical analysis section with electrodes and a separate optical fiber sensing section. The optical fiber probe has a conductive layer with glucose oxidase covering it. This layer is electrically connected to the working electrode. By placing both the reference electrode and the fiber probe in the same solution, simultaneous electrochemical and optical fiber glucose measurements can be made.

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A low-cost, scalable sensor chip for simultaneously monitoring multiple diabetes biomarkers like glucose, insulin, cortisol, etc. from a single drop of bodily fluid. The chip integrates enzymatic and immunosensing electrochemical detection methods onto a single substrate. This allows simultaneous detection of multiple analytes like glucose and insulin in a compact, wearable form. The chip is fabricated using a scalable, non-lithography method. It enables decentralized, point-of-care monitoring of diabetes biomarkers in blood, saliva, etc.

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Monitoring glucose levels in patients using sensors to reduce measurement errors caused by interferents without adding materials or devices. The method involves detecting interferents like acetaminophen in body fluid near the sensor and correcting glucose measurements accordingly. It uses low frequency electrochemical impedance spectroscopy (EIS) signals to identify interferent presence and concentration. The sensor signals and changes are used to build a model that corrects glucose measurements for interferent bias. The voltage transmitted to the sensor can also be reduced in response to interferent detection.

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Closed-loop insulin infusion systems using orthogonally redundant glucose sensors for improved accuracy and reliability. The system has two glucose sensors, one optical and one electrochemical, to provide orthogonal redundancy. An algorithm combines the sensor data to improve accuracy and reliability. If one sensor fails, the other can provide glucose values. The sensors have features like distributed electrodes and membrane barriers to reduce drift and fouling. The system uses on-demand calibration rather than frequent fingersticks.

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Continuous glucose monitoring (CGM) system that improves reliability and reduces calibration needs by detecting medication interferences and correcting glucose readings. The system uses two electrodes on a user, one with glucose oxidase (GOx) and one without, set to different voltages. Comparing signals from both electrodes allows detecting medication ingestion that affects GOx. This signal comparison is used to adjust CGM glucose readings. The second electrode serves as a background reference to isolate GOx interference.

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Wearable health monitoring devices that can simultaneously measure electrocardiogram (ECG) and bioimpedance (BI) using just one or two pairs of shared electrodes. This allows smaller, more inconspicuous wearables with fewer electrodes than traditional systems. The shared electrodes are used to extract ECG and BI measurements from a single sensed signal. An injection current is provided through one set of electrodes while the other set is used for sensing. This enables ECG and BI measurements without additional electrodes, reducing size and weight.

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Detecting continuous analyte sensors like glucose sensors for accurate and reliable monitoring by compensating for errors due to factors like warm-up time, sensitivity changes, and background interference. The method involves applying a detection potential modulation sequence between primary current measurements to extract sensor condition information. This data is used to calculate a correction index that adjusts the primary current readings to account for factors like sensitivity drift.

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A system for interpreting and managing bioanalyte measurements from wearable devices like glucose sensors. The system involves collecting data from interstitial bioanalyte sensors, interpreting it to account for delays compared to capillary blood measurements, and sending the interpreted results to an external device with dedicated software to provide useful information to the user. The system aims to leverage wearable sensor technology for continuous monitoring of interstitial bioanalytes like glucose, but addresses challenges like delayed response compared to capillary blood measurements by interpreting and managing the data to provide more clinically relevant information to users.

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Non-invasive blood glucose meter that doesn't require blood sampling for diabetes monitoring. The meter uses impedance measurements from sensors on the skin to detect glucose levels. It scans multiple frequencies to measure impedance changes and correlates them with glucose levels. The meter also has pressure and blood flow sensors to ensure consistent contact conditions for accurate readings. This addresses issues like variation in impedance measurements due to contact pressure, moisture, and contact area. The meter notifies the user when the contact pressure is optimal for consistent readings.

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Mobile case for smartphones that enables non-intrusive physiological monitoring using integrated sensors. The case has recesses with optical sensors and electrodes for bioimpedance measurements. This allows continuous, touchless monitoring of parameters like body composition, hydration, heart health, and glucose levels by leveraging the phone's existing touchscreen. The case captures daily data for improved reliability and uses machine learning to filter out noise. Statistical analysis detects trends for health insights.

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Enhancing the accuracy of glucose sensors used in continuous glucose monitoring (CGM) systems for diabetes management by leveraging information from insulin pumps to improve sensor accuracy, particularly during hypoglycemia where CGM accuracy is lowest. The method involves using insulin delivery data along with glucose sensor readings and a filtering algorithm to estimate glucose levels. This estimated glucose is then weighted more heavily than the sensor reading during hypoglycemia to account for the sensor's accuracy issues in that range. The weighting scheme balances sensor and estimated glucose based on factors like insulin delivery and sensor error indices.

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System to securely collect, analyze, and report continuous glucose monitoring (CGM) data from multiple monitors. The system involves a distributed architecture with CGM devices, display devices, cloud servers, and an analysis engine. The data is classified by sensitivity and selectively transmitted through the architecture to control access to restricted data like patient IDs. Encryption and common APIs are used.

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A single-probe, multi-analyte continuous glucose monitoring system for improved automated insulin delivery in diabetes management. The system has a minimally invasive probe that measures glucose and at least one other analyte like lactate or ketones. Integrating multiple analytes and optional physical sensors into the probe, it provides contextual information about physiological states like meals, exercise, stress, and sleep. This seamless data stream from a single probe reduces the burden compared to multiple device insertions. The system uses the combined signals to refine personalized insulin delivery and artificial pancreas algorithms for closed-loop control. It enables 24/7 automation without needing separate devices for each analyte.

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