Abstract: Systems and methods for remote and host monitoring communication are disclosed. In some implementations, monitoring systems can comprise a host monitoring device associated with a Host communicatively coupled to one or more remote monitoring devices associated with Remote Monitors. The host monitoring device can send communications based at least in part on analyte measurements of a Host sensor and/or other contextual data giving such measurements context. Different remote monitoring devices can receive different communications based at least in part on the role of the respective Remote Monitors relative to the Host. These roles can be reflected in classifications of Remote Monitors.

Abstract: Various examples are directed to systems and methods for monitoring a patient. For example, patient data of a first type may be monitored with a low-fidelity monitoring technique over a first time period. Patient behavior may be evaluated based on the monitored first type of patient data. Patient data of a second type may be monitored over a second time period with a continuous glucose monitor device. A parameter of the continuous glucose monitor device may be based on the evaluated patient behavior during the monitoring.

Abstract: Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. The plurality of risk factors may be selected from the list including the number of days the sensor has been in use, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and error between reference BG values and EGV sensor values.

Abstract: This disclosure provides systems, methods and apparatus for processing, transmitting and displaying data received from an analyte sensor, such as a glucose sensor. The system may include a display device with at least one input device. In response to movement of or along the input device, the display device may change a glucose data output parameter and update an output of the display device using the changed output parameter.

Abstract: Systems and methods are provided for detecting changes or fluctuations in an analyte concentration signal that are abnormal, e.g., exceed a predetermined threshold, current trend of analyte concentration measurements, etc. Signals indicative of an analyte concentration in a host may be received from an analyte sensor. The signals may be monitored, and a determination can be made as to whether there is a change in the signal. Upon detecting such a change, the change can be compensated for such that a representation of the signal indicates the analyte concentration. Optionally, the cause of the detected changes or fluctuations can also be determined and information regarding the detected changes or fluctuations can be recorded and analyzed for subsequent optimization of the systems and methods as well for transmitting alerts, notifications, etc. to a user to take corrective action.

Abstract: Systems, devices, and methods are disclosed for wireless communication of analyte data. In this regard, in embodiments, a mobile includes a transceiver configured to transmit and receive wireless signals. The mobile device includes circuitry operatively coupled to the transceiver. The mobile device also includes a non-transitory computer-readable medium operatively coupled to the circuitry and storing instructions that, when executed, cause the mobile device to perform a number of operations. One such operation is to obtain a derivative of a first signal received via a first link. Another such operation is to obtain a derivative of a second signal received via a second link; and. Yet another such operation is to generate a selection for connection to an analyte sensor system, based on a comparison of the derivative of the first signal and the derivative of the second signal.

Abstract: Systems, devices, and methods are disclosed for wireless communication of analyte data. One such method includes, during a first interval, establishing a first connection between an analyte sensor system and a display device. During the first connection, the method includes exchanging information related to authentication between the analyte sensor system and the display device. The method includes making a determination regarding whether authentication was performed during the first interval. During a second interval, the method may include establishing a second connection between the analyte sensor system and the display device for transmission of an encrypted analyte value, and bypassing the exchanging of information related to authentication performed during the first connection.

Abstract: Disclosed are systems and methods for secure and seamless set up and modification of bolus calculator parameters for a bolus calculator tool by a health care provider (HCP). In one aspect, a method for enabling HCP set up of a bolus calculator includes providing a server accessible by both an HCP and a patient; upon login by the HCP, displaying, or transmitting for display, a fillable form, the fillable form including one or more fields for entry of one or more bolus calculator parameters; receiving data from the fillable form, the data corresponding to one or more bolus calculator parameters; and upon login by the patient, transmitting data to a device associated with the patient, the transmitted data based on the received data, where the transmitted data corresponds to one or more of the bolus calculator parameters in a format suitable for entry to a bolus calculator.

Abstract: Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. The plurality of risk factors may be selected from the list including the number of days the sensor has been in use, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and error between reference BG values and EGV sensor values.

Abstract: Various embodiments relate generally to continuous monitoring of analyte values received from an analyte sensor system. In some example embodiments, there is provided a method that includes receiving sensor information, calculating and storing estimated analyte measurement values based upon the received sensor information. The method also includes determining one or more communication conditions, and instructing a transceiver to advertise to a first display device in accordance with one or more communication variables based upon the one or more communication conditions. The method then transmits the estimated analyte measurement values to the at least first display device. Related systems, methods, and articles of manufacture are also described.

Abstract: Disclosed are systems and methods for secure and seamless set up and modification of bolus calculator parameters for a bolus calculator tool by a health care provider (HCP). In one aspect, a method for enabling HCP set up of a bolus calculator includes providing a server accessible by both an HCP and a patient; upon login by the HCP, displaying, or transmitting for display, a fillable form, the fillable form including one or more fields for entry of one or more bolus calculator parameters; receiving data from the fillable form, the data corresponding to one or more bolus calculator parameters; and upon login by the patient, transmitting data to a device associated with the patient, the transmitted data based on the received data, where the transmitted data corresponds to one or more of the bolus calculator parameters in a format suitable for entry to a bolus calculator.

Abstract: Methods, devices and systems are disclosed for inter-app communications between software applications on a mobile communications device. In one aspect, a computer-readable medium on a mobile computing device comprising an inter-application communication data structure to facilitate transitioning and distributing data between software applications in a shared app group for an operating system of the mobile computing device includes a scheme field of the data structure providing a scheme id associated with a target software app to transition to from a source software app, wherein the scheme id is listed on a scheme list stored with the source software app; and a payload field of the data structure providing data and/or an identification where to access data in a shared file system accessible to the software applications in the shared app group, wherein the payload field is encrypted.

Abstract: Various embodiments relate generally to continuous monitoring of analyte values received from an analyte sensor system. In some example embodiments, there is provided a method that includes receiving sensor information, calculating and storing estimated analyte measurement values based upon the received sensor information. The method also includes determining one or more communication conditions, and instructing a transceiver to advertise to a first display device in accordance with one or more communication variables based upon the one or more communication conditions. The method then transmits the estimated analyte measurement values to the at least first display device. Related systems, methods, and articles of manufacture are also described.

Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

Abstract: Systems, devices, and methods are disclosed for wireless communication of analyte data. In this regard, in embodiments, a mobile includes a transceiver configured to transmit and receive wireless signals. The mobile device includes circuitry operatively coupled to the transceiver. The mobile device also includes a non-transitory computer-readable medium operatively coupled to the circuitry and storing instructions that, when executed, cause the mobile device to perform a number of operations. One such operation is to obtain a derivative of a first signal received via a first link. Another such operation is to obtain a derivative of a second signal received via a second link; and. Yet another such operation is to generate a selection for connection to an analyte sensor system, based on a comparison of the derivative of the first signal and the derivative of the second signal.

Abstract: Systems, devices, and methods are disclosed for wireless communication of analyte data. In this regard, in embodiments, a mobile includes a transceiver configured to transmit and receive wireless signals. The mobile device includes circuitry operatively coupled to the transceiver. The mobile device also includes a non-transitory computer-readable medium operatively coupled to the circuitry and storing instructions that, when executed, cause the mobile device to perform a number of operations. One such operation is to obtain a derivative of a first signal received via a first link. Another such operation is to obtain a derivative of a second signal received via a second link; and. Yet another such operation is to generate a selection for connection to an analyte sensor system, based on a comparison of the derivative of the first signal and the derivative of the second signal.