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 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: Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device capable of delivering fluid to a user involves determining one or more signal characteristics associated with a subset of the measurements corresponding to a monitoring period, determining a reliability metric for the monitoring period based on the one or more signal characteristics, and providing indication of a maintenance condition when the reliability metric violates a threshold.

Abstract: Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device involves obtaining a filtered measurement indicative of a physiological condition of a user, determining a metric indicative of a characteristic of the filtered measurement based at least in part on one or more derivative metrics associated with the filtered measurement, and determining an output measurement indicative of the physiological condition of the user based at least in part on the filtered measurement, the metric, and a previous output measurement.

Abstract: Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device involves obtaining a filtered measurement indicative of a physiological condition of a user, determining a metric indicative of a characteristic of the filtered measurement based at least in part on one or more derivative metrics associated with the filtered measurement, and determining an output measurement indicative of the physiological condition of the user based at least in part on the filtered measurement, the metric, and a previous output measurement.

Abstract: Subject matter disclosed herein relates to monitoring and/or controlling levels of an analyte in bodily fluid. In particular, estimation of a concentration of the analyte in a first physiological compartment based upon observations of a concentration of the analyte in a second physiological compartment may account for a latency in transporting the analyte between the first and second physiological compartments.

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 Programming (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: 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: 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: Medical devices and related patient management systems and parameter modeling methods are provided. An exemplary method involves obtaining, by a computing device, historical measurements of a condition in a body of the patient previously provided by a sensing device, obtaining, by the computing device, historical operational context information associated with preceding operation of one or more of an infusion device and the sensing device, obtaining, by the computing device, historical values for a parameter from one or more of the infusion device and the sensing device, determining, by the computing device a patient-specific model of the parameter based on relationships between the historical measurements, the historical operational context information and the historical values, and providing, by the computing device via a network, the patient-specific model to one of the infusion device, the sensing device or a client device.

Abstract: The subject matter disclosed herein relates to systems, methods and/or devices for calibrating sensor data to be used in estimating a blood glucose concentration. A relationship between sensor signal values and reference readings may be used to estimate a relationship between sensor signal values and measurements of blood glucose concentration.

Abstract: Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device involves obtaining a filtered measurement indicative of a physiological condition of a user, determining a metric indicative of a characteristic of the filtered measurement based at least in part on one or more derivative metrics associated with the filtered measurement, and determining an output measurement indicative of the physiological condition of the user based at least in part on the filtered measurement, the metric, and a previous output measurement.

Abstract: Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device capable of delivering fluid to a user involves determining one or more signal characteristics associated with a subset of the measurements corresponding to a monitoring period, determining a reliability metric for the monitoring period based on the one or more signal characteristics, and providing indication of a maintenance condition when the reliability metric violates a threshold.

Abstract: Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device capable of delivering fluid to a user involves obtaining one or more uncalibrated measurements indicative of the physiological condition, obtaining one or more reference measurements of the physiological condition, determining a raw calibration factor based on a relationship between the one or more uncalibrated measurements and the one or more reference measurements corresponding to the respective uncalibrated measurements of the one or more uncalibrated measurements, and determining an adjusted calibration factor based at least in part on an expected calibration factor and the raw calibration factor, wherein operation of the infusion device to deliver the fluid is influenced by the adjusted calibration factor.

Abstract: Subject matter disclosed herein relates to monitoring and/or controlling levels of an analyte in bodily fluid. In particular, estimation of a concentration of the analyte in a first physiological compartment based upon observations of a concentration of the analyte in a second physiological compartment may account for a latency in transporting the analyte between the first and second physiological compartments.