Abstract: A method of optimizing operation of a glucose sensor includes performing an electrochemical impedance spectroscopy (EIS) procedure to obtain imaginary impedance values for an electrode of a glucose sensor, calculating a change value as a difference between a threshold reference for the imaginary impedance values and a most-recent imaginary impedance value, and obtaining measurements of the calibration factor for the glucose sensor. The method also includes comparing the change value to a first threshold and the calibration factor to a second threshold and determining, based on the comparison, whether sensor data from the glucose sensor is valid.

Abstract: A continuous glucose monitoring system may utilize electrode current (Isig) signals, Electrochemical Impedance Spectroscopy (EIS), and Vcntr values to optimize sensor glucose (SG) calculation in such a way as to enable reduction of the need for blood glucose (BG) calibration requests from users.

Abstract: Electrochemical impedance spectroscopy (EIS) may be used in conjunction with continuous glucose monitoring (CGM) to enable identification of valid and reliable sensor data, as well implementation of Smart Calibration algorithms.

Abstract: Electrochemical impedance spectroscopy (EIS) may be used in conjunction with continuous glucose monitoring (CGM) to enable identification of valid and reliable sensor data, as well implementation of Smart Calibration algorithms.

Abstract: A system includes one or more processors and one or more processor-readable storage media storing instructions which, when executed by the one or more processors, cause performance of receiving, via a user interface, confirmation input that indicates user acceptance of a blood glucose (BG) measurement generated by a BG meter device, in response to receiving the confirmation input, using the BG measurement for performing both glucose sensor calibration and bolus calculation, and causing delivery of a bolus based on outputting a delivery command in accordance with the bolus calculation.

Abstract: A method for retrospective calibration of a glucose sensor uses stored values of measured working electrode current (Isig) to calculate a final sensor glucose (SG) value retrospectively. The Isig values may be preprocessed, discrete wavelet decomposition applied. At least one machine learning model, such as, e.g., Genetic Programing (GP) and Regression Decision Tree (DT), may be used to calculate SG values based on the Isig values and the discrete wavelet decomposition. Other inputs may include, e.g., counter electrode voltage (Vcntr) and Electrochemical Impedance Spectroscopy (EIS) data. A plurality of machine learning models may be used to generate respective SG values, which are then fused to generate a fused SG. Fused SG values may be filtered to smooth the data, and blanked if necessary.

Abstract: Electrochemical impedance spectroscopy (EIS) may be used in conjunction with continuous glucose monitoring (CGM) to enable identification of valid and reliable sensor data, as well implementation of Smart Calibration algorithms.

Abstract: A continuous glucose monitoring system may employ complex redundancy to take operational advantage of disparate characteristics of two or more dissimilar, or non-identical, sensors, including, e.g., characteristics relating to hydration, stabilization, and durability of such sensors. Fusion algorithms, Electrochemical Impedance Spectroscopy (EIS), and advanced Application Specific Integrated Circuits (ASICs) may be used to implement use of such redundant glucose sensors, devices, and sensor systems in such a way as to bridge the gaps between fast start-up, sensor longevity, and accuracy of calibration-free algorithms. Systems, devices, and algorithms are described for achieving a long-wear and reliable sensor which also minimizes, or eliminates, the need for BG calibration, thereby providing a calibration-free, or near calibration-free, sensor.

Abstract: Electrochemical impedance spectroscopy (EIS) may be used in conjunction with continuous glucose monitoring (CGM) to enable identification of valid and reliable sensor data, as well implementation of Smart Calibration algorithms.

Abstract: A system for monitoring a patient includes one or more processors and a sensor device implemented in circuitry. The system is configured to measure, using the sensor device, a blood glucose status of the patient, and determine, using one or more processors, an initial insulin dose for the patient based on the blood glucose status of the patient. The system is further configured to optimize the initial insulin dose for the patient, based at least in part on a correction factor, to create an optimized insulin dose for the patient. The system is configured to facilitate therapy, using the one or more processors, based on the determined optimized insulin dose.

Abstract: A method for optional external calibration of a calibration-free glucose sensor uses values of measured working electrode current (Isig) and EIS data to calculate a final sensor glucose (SG) value. Counter electrode voltage (Vcntr) may also be used as an input. Raw Isig and Vcntr values may be preprocessed, and low-pass filtering, averaging, and/or feature generation may be applied. SG values may be generated using one or more models for predicting SG calculations. When an external blood glucose (BG) value is available, the BG value may also be used in calculating the SG values. A SG variance estimate may be calculated for each predicted SG value and modulated, with the modulated SG values then fused to generate a fused SG. A Kalman filter, as well as error detection logic, may be applied to the fused SG value to obtain a final SG, which is then displayed to the user.

Abstract: A method for optional external calibration of a calibration-free glucose sensor uses values of measured working electrode current (Isig) and EIS data to calculate a final sensor glucose (SG) value. Counter electrode voltage (Vcntr) may also be used as an input. Raw Isig and Vcntr values may be preprocessed, and low-pass filtering, averaging, and/or feature generation may be applied. SG values may be generated using one or more models for predicting SG calculations. When an external blood glucose (BG) value is available, the BG value may also be used in calculating the SG values. A SG variance estimate may be calculated for each predicted SG value and modulated, with the modulated SG values then fused to generate a fused SG. A Kalman filter, as well as error detection logic, may be applied to the fused SG value to obtain a final SG, which is then displayed to the user.

Abstract: A system includes one or more processors and one or more processor-readable storage media storing instructions which, when executed by the one or more processors, cause performance of receiving, via a user interface, confirmation input that indicates user acceptance of a blood glucose (BG) measurement generated by a BG meter device, in response to receiving the confirmation input, using the BG measurement for performing both glucose sensor calibration and bolus calculation, and causing delivery of a bolus based on outputting a delivery command in accordance with the bolus calculation.

Abstract: An insulin infusion device and a related operating method are disclosed. The method obtains a blood glucose (BG) measurement of a blood sample from a user, displays the BG measurement on a display element of the infusion device, and receives a confirmation input that indicates user acceptance of the displayed BG measurement. In response to the confirmation input, a glucose sensor calibration routine is automatically initiated, and an insulin bolus calculation is automatically initiated. The calibration routine and the bolus calculation use the confirmed BG measurement. The method continues by controlling delivery of insulin, in accordance with the calculated insulin bolus, from a fluid reservoir of the insulin infusion device. The sensor calibration routine and the insulin bolus calculation are both performed without any additional user involvement or interaction with the user interface other than the user acceptance of the displayed BG measurement.

Abstract: A health care provider (HCP) assistant system includes at least one processor device and a processor-readable medium that stores executable instructions configurable to cause the at least one processor device to perform a method that collects and maintains medical device user data that includes data associated with a patient under the care of an HCP customer. The method also collects and maintains HCP data that includes data associated with the HCP customer. The collected medical device user data and the collected HCP data is reviewed to automatically generate content to assist the HCP customer in managing treatment of the patient. The generated content is provided to an HCP user system for presentation to the HCP customer.

Abstract: A continuous glucose monitoring system may utilize electrode current (Isig) signals, Electrochemical Impedance Spectroscopy (EIS), and Vcntr values to optimize sensor glucose (SG) calculation in such a way as to enable reduction of the need for blood glucose (BG) calibration requests from users.

Abstract: An insulin infusion device and a related operating method are disclosed. The method obtains a blood glucose (BG) measurement of a blood sample from a user, displays the BG measurement on a display element of the infusion device, and receives a confirmation input that indicates user acceptance of the displayed BG measurement. In response to the confirmation input, a glucose sensor calibration routine is automatically initiated, and an insulin bolus calculation is automatically initiated. The calibration routine and the bolus calculation use the confirmed BG measurement. The method continues by controlling delivery of insulin, in accordance with the calculated insulin bolus, from a fluid reservoir of the insulin infusion device. The sensor calibration routine and the insulin bolus calculation are both performed without any additional user involvement or interaction with the user interface other than the user acceptance of the displayed BG measurement.

Abstract: Electrochemical impedance spectroscopy (EIS) may be used in conjunction with continuous glucose monitoring (CGM) to enable identification of valid and reliable sensor data, as well implementation of Smart Calibration algorithms.

Abstract: Electrochemical impedance spectroscopy (EIS) may be used in conjunction with continuous glucose monitoring (CGM) to enable identification of valid and reliable sensor data, as well implementation of Smart Calibration algorithms.

Abstract: Electrochemical impedance spectroscopy (EIS) may be used in conjunction with continuous glucose monitoring (CGM) to enable identification of valid and reliable sensor data, as well implementation of Smart Calibration algorithms.