Abstract: Techniques for improving continuous glucose monitoring (“CGM”) are described herein. In some embodiments, the techniques involve obtaining sensor data; applying, to the sensor data, a machine learning model trained to identify sensor data error patterns; and detecting an erroneous sensor use condition based on output of the machine learning model indicating an error pattern identified in the sensor data.

Abstract: Medical devices and related systems and methods are provided. A method of controlling medication delivery based on sensor input involves obtaining a measurement parameter representing an electrical response of a first instance of a sensing element to a physiological condition of a person. The measurement parameter is converted into a calibrated measurement parameter using calibration data specific to the first instance of the sensing element. The method further involves determining a measurement value using the calibrated measurement parameter as input to a performance model. The performance model is derived from historical calibrated measurement parameters and corresponding reference values. The historical calibrated measurement parameters are from other instances of the sensing element. A command is then determined based on the measurement value and sent to a medical device. The command causes the medical device to deliver a dose of medication influencing the physiological condition of the person.

Abstract: Techniques disclosed herein relate to determining a calibrated measurement value indicative of a physiological condition of a patient using sensor calibration data determined based on fabrication measurements. In some embodiments, the techniques involve obtaining one or more electrical signals from a sensing element of a sensing arrangement, where the one or more electrical signals are influenced by a physiological condition in a body of a patient; obtaining calibration data associated with the sensing element, where the calibration data is based on fabrication process measurement data for the sensing element and a calibration model for a certain physiological condition; and determining, using the one or more electrical signals and the calibration data associated with the sensing element, a calibrated output value indicative of the physiological condition.

Abstract: Techniques for determining glucose sensitivity are provided. In some embodiments, the techniques may involve receiving sensor data relating to a sensor electrical property. The techniques may further involve determining a subspace of a plurality of subspaces of an input signal feature space based on a respective range of values associated with the sensor electrical property. The techniques may further involve selecting a machine learning model from a plurality of machine learning models associated with the subspace. The techniques may further involve determining a glucose sensitivity of a glucose sensor device based on the sensor data and the selected machine learning model. The techniques may further involve determining whether to inhibit or utilize glucose readings of the glucose sensor device based on the glucose sensitivity. The techniques may further involve operating the glucose sensor device based on the determination.

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: Techniques disclosed herein relate to determining a calibrated measurement value indicative of a physiological condition of a patient using sensor calibration data determined based on fabrication measurements. In some embodiments, the techniques involve obtaining one or more electrical signals from a sensing element of a sensing arrangement, where the one or more electrical signals are influenced by a physiological condition in a body of a patient; obtaining calibration data associated with the sensing element, where the calibration data is based on fabrication process measurement data for the sensing element and a calibration model for a certain physiological condition; and determining, using the one or more electrical signals and the calibration data associated with the sensing element, a calibrated output value indicative of the physiological condition.

Abstract: Medical devices and related systems and methods are provided. A method of controlling medication delivery based on sensor input involves obtaining a measurement parameter representing an electrical response of a first instance of a sensing element to a physiological condition of a person. The measurement parameter is converted into a calibrated measurement parameter using calibration data specific to the first instance of the sensing element. The method further involves determining a measurement value using the calibrated measurement parameter as input to a performance model. The performance model is derived from historical calibrated measurement parameters and corresponding reference values. The historical calibrated measurement parameters are from other instances of the sensing element. A command is then determined based on the measurement value and sent to a medical device. The command causes the medical device to deliver a dose of medication influencing the physiological condition of the person.

Abstract: Methods, systems, and devices for modeling a relationship between glucose sensitivity and a sensor electrical property are described herein. More particularly, the methods, systems, and devices describe partitioning an input signal feature space relating glucose sensitivity and a sensor electrical property into subspaces and training a model for each subspace. For example, the subspace models may form a mosaic of models, for which the output is more accurate than a single model.

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 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: Methods, systems, and devices for improving continuous glucose monitoring (“CGM”) are described herein. More particularly, the methods, systems, and devices describe applying micro machine learning models to generate predicted sensor glucose values. The system may use the predicted sensor glucose values to display a sensor glucose value to a user. The layered models may generate more reliable sensor glucose predictions across many scenarios, leading to a reduction of sensor glucose signal blanking. The methods, systems, and devices described herein further comprise applying a plurality of micro model to estimate sensor glucose values under outlier conditions. The system may prioritize the models that are trained for certain outlier conditions when the system detects those outlier condition based on the sensor data.

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: A method of estimating a value of a physiological condition may be performed by an electronic device including one or more processors. The method involves generating a simulated measurement using an actual measurement from a first sensor as input to a translation model. The actual measurement includes one or more measurement parameters output by the first sensor as an indication of the value of the physiological condition. The simulated measurement includes one or more measurement parameters that a second sensor would output given the same value of the physiological condition. The method further involves estimating the value of the physiological condition through inputting the simulated measurement to an estimation model. The estimation model is configured to map the one or more measurement parameters that the second sensor would output to an estimated value for the physiological condition.

Abstract: Methods, systems, and devices for improving continuous glucose monitoring (“CGM”) are described herein. More particularly, the methods, systems, and devices describe applying micro machine learning models to generate predicted sensor glucose values. The system may use the predicted sensor glucose values to display a sensor glucose value to a user. The layered models may generate more reliable sensor glucose predictions across many scenarios, leading to a reduction of sensor glucose signal blanking. The methods, systems, and devices described herein further comprise applying a plurality of micro model to estimate sensor glucose values under outlier conditions. The system may prioritize the models that are trained for certain outlier conditions when the system detects those outlier condition based on the sensor data.

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: Methods, systems, and devices for improving continuous glucose monitoring (“CGM”) are described herein. More particularly, the methods, systems, and devices describe applying layered machine learning models to generate predicted sensor glucose values. The system may use the predicted sensor glucose values to display a sensor glucose value to a user. The layered models may generate more reliable sensor glucose predictions across many scenarios, leading to a reduction of sensor glucose signal blanking. The methods, systems, and devices described herein further comprise applying a plurality of micro model to estimate sensor glucose values under outlier conditions. The system may prioritize the models that are trained for certain outlier conditions when the system detects those outlier condition based on the sensor data.

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: Medical devices and related systems and methods are provided. A method of estimating a physiological condition using a first sensing arrangement involves obtaining a sensor translation model associated with a relationship between the first sensing arrangement and a second sensing arrangement, wherein the second sensing arrangement is different from the first sensing arrangement, obtaining one or more measurements from a sensing element coupled to the processing system of the first sensing arrangement, determining simulated measurement data for the second sensing arrangement by applying the sensor translation model to the one or more measurements from the sensing element of the first sensing arrangement, and determining an estimated value for the physiological condition by applying an estimation model associated with the second sensing arrangement to the simulated measurement data.