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) is used in conjunction with continuous glucose monitors and continuous glucose monitoring (CGM) to enable in-vivo sensor calibration, gross (sensor) failure analysis, and intelligent sensor diagnostics and fault detection. An equivalent circuit model is defined, and circuit elements are used to characterize sensor behavior.

Abstract: A processor-implemented method comprises obtaining current operational context information associated with a sensing device; obtaining an expected calibration factor parameter model associated with a patient; calculating an expected calibration factor value based on the expected calibration factor parameter model and the current operational context information; obtaining one or more electrical signals from the sensing device, the one or more electrical signals having a signal characteristic indicative of a physiological condition; converting the one or more electrical signals into a calibrated measurement value for the physiological condition using the expected calibration factor value; and outputting the calibrated measurement value for the physiological condition.

Abstract: Electrochemical Impedance Spectroscopy (EIS) is used in conjunction with continuous glucose monitors and continuous glucose monitoring (CGM) to enable in-vivo sensor calibration, gross (sensor) failure analysis, and intelligent sensor diagnostics and fault detection. An equivalent circuit model is defined, and circuit elements are used to characterize sensor behavior.

Abstract: A method of automatically initializing an analyte sensor for a user is disclosed here. A first analyte sensor is operated in a first measurement mode to generate first sensor signals indicative of an analyte level of the user. A second analyte sensor is deployed to measure the analyte level of the user, and is operated in an initialization mode, concurrently with operation of the first analyte sensor in the first measurement mode, to receive sensor configuration data generated by the first analyte sensor. During operation of the second analyte sensor in the initialization mode, the second analyte sensor is calibrated with at least some of the received sensor configuration data. After the calibrating, operation of the second analyte sensor is transitioned from the initialization mode to a second measurement mode during which the second analyte sensor generates second sensor signals indicative of the analyte level of the user.

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: Techniques for sensor calibration involve determining a generative model using sensor measurements from different instances of a first glucose sensor together with corresponding reference glucose values. The generative model is configured to generate a simulated measurement representing a predicted output of the first glucose sensor under specific operating conditions. A set of simulated measurements is generated using operating conditions observed with respect to a second glucose sensor as inputs to the generative model. The second glucose sensor is a sensor of a different design. The operating conditions observed with respect to the second glucose sensor include reference glucose values obtained in connection with measurements made using the second glucose sensor. The simulated measurements are then used to determine an estimation model for the first glucose sensor. The estimation model is configured to estimate glucose level given one or more sensor measurements from the first glucose sensor.

Abstract: A fluid infusion system includes a housing configured to be adhesively coupled to an anatomy of a user, and a tube configured to extend from the housing for insertion into the anatomy of the user. The tube includes a plurality of conduits defined within the tube. The plurality of conduits include a fluid delivery conduit configured to facilitate a fluidic connection between a fluid source and the anatomy of the user, and one or more conduits configured to accommodate a plurality of electrodes for determining a physiological characteristic of the user.

Abstract: Methods and systems for sensor calibration and sensor glucose (SG) fusion are used advantageously to improve the accuracy and reliability of orthogonally redundant glucose sensor devices, which may include optical and electrochemical glucose sensors. Calibration for both sensors may be achieved via fixed-offset and/or dynamic regression methodologies, depending, e.g., on sensor stability and Isig-Ratio pair correlation. For SG fusion, respective integrity checks may be performed for SG values from the optical and electrochemical sensors, and the SG values calibrated if the integrity checks are passed. Integrity checks may include checking for sensitivity loss, noise, and drift. If the integrity checks are failed, in-line sensor mapping between the electrochemical and optical sensors may be performed prior to calibration.

Abstract: Disclosed are methods and corresponding systems and devices for providing an estimation model for use with one or more instances of a particular sensor. In some aspects, an estimation model usable for estimating a value of a physiological condition is determined based at least in part on simulated measurements. The simulated measurements are generated for a first sensor, through applying a translation model to convert historical measurements associated with a second sensor into measurements that would have been produced by the first sensor. The second sensor has a different design or configuration than the first sensor. The historical measurements represent changes in the physiological condition as observed by different instances of the second sensor. The estimation model can be made available to one or more electronic devices, including at least one device configured to apply the estimation model to a measurement from a corresponding instance of the first sensor.

Abstract: Electrochemical Impedance Spectroscopy (EIS) is used in conjunction with continuous glucose monitors and continuous glucose monitoring (CGM) to enable in-vivo sensor calibration, gross (sensor) failure analysis, and intelligent sensor diagnostics and fault detection. An equivalent circuit model is defined, and circuit elements are used to characterize sensor behavior.

Abstract: Medical devices and related systems and methods are provided. A method of estimating a physiological condition involves determining a translation model based at least in part on relationships between first measurement data corresponding to instances of a first sensing arrangement and second measurement data corresponding to instances of a second sensing arrangement, obtaining third measurement data associated with the second sensing arrangement, determining simulated measurement data for the first sensing arrangement by applying the translation model to the third measurement data, and determining an estimation model for a physiological condition using the simulated measurement data, wherein the estimation model is applied to subsequent measurement output provided by an instance of the first sensing arrangement to obtain an estimated value for the physiological condition.

Abstract: Electrochemical Impedance Spectroscopy (EIS) is used in conjunction with continuous glucose monitors and continuous glucose monitoring (CGM) to enable in-vivo sensor calibration. gross (sensor) failure analysis, and intelligent sensor diagnostics and faith detection. An equivalent circuit model is defined, and circuit elements are used to characterize sensor behavior.

Abstract: A fluid infusion system includes a housing configured to be adhesively coupled to an anatomy of the user. The housing comprises a communication device configured to wirelessly communicate a physiological characteristic to a communication component of a fluid infusion device. The fluid infusion system includes a fluid flow path from the fluid infusion device into the anatomy of the user, and the fluid flow path is configured to extend from the housing for insertion into the anatomy of the user.

Abstract: A processor-implemented method comprises obtaining current operational context information associated with a sensing device; obtaining an expected calibration factor parameter model associated with a patient; calculating an expected calibration factor value based on the expected calibration factor parameter model and the current operational context information; obtaining one or more electrical signals from the sensing device, the one or more electrical signals having a signal characteristic indicative of a physiological condition; converting the one or more electrical signals into a calibrated measurement value for the physiological condition using the expected calibration factor value; and outputting the calibrated measurement value for the physiological condition.

Abstract: Electrochemical Impedance Spectroscopy (EIS) is used in conjunction with continuous glucose monitors and continuous glucose monitoring (CGM) to enable in-vivo sensor calibration, gross (sensor) failure analysis, and intelligent sensor diagnostics and fault detection. An equivalent circuit model is defined, and circuit elements are used to characterize sensor behavior.

Abstract: The disclosed techniques include obtaining a first signal generated by an electrochemical glucose sensor and a second signal generated by an optical glucose sensor, and obtaining a glucose value indicative of a user's blood glucose level, where the glucose value and the second signal are obtained at different times. The disclosed techniques further cause calculating a mapped value for the second signal based on the first signal, and calibrating the mapped value of the second signal based on the glucose value.

Abstract: Medical devices and related patient management systems and parameter modeling methods are provided. An exemplary method of operating a sensing device associated with a patient involves obtaining current operational context information associated with the sensing device, obtaining a parameter model associated with the patient, calculating a current parameter value based on the parameter model and the current operational context information, obtaining one or more signals from a sensing element configured to measure a condition in a body of the patient, and providing an output that is influenced by the calculated current parameter value and the one or more signals.

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.