Abstract: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects.

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 utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects.

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: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects.

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 utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects.

Abstract: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects.

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: 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.

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 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 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: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects.

Abstract: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects.

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.