56 results about "Glucose monitors" patented technology

The present disclosure presents glucose monitoring systems and methods. One such system comprises a glucose monitoring sensor having at least a counter electrode, a reference electrode, and a working electrode; and a silicon-carbide-silicon-carbide-nanoparticles-electrospun-nanofibrous-membrane contacting the working electrode. The system further includes a glucose oxidase enzyme solution contacting the silicon-carbide-silicon-carbide-nanoparticles-electrospun-nanofibrous-membrane, wherein a conductive polymer membrane assists in binding the glucose oxidase enzyme composition with the silicon-carbide-silicon-carbide-nanoparticles-electrospun-nanofibrous-membrane. Other systems and methods are also provided.

This invention provides a precise control structure and applicator for the indwelling device in a continuous glucose monitoring (CGM) meter. By incorporating a clock structure consisting of an unlocking control track and a control block, different areas on the unlocking control track apply varying forces to the control block. These forces switch precisely upon completion of the puncture, allowing the clock structure to correlate with the puncture process. This enables the device to sense the puncture and promptly unlock the indwelling device after the procedure. This mechanical structure offers precise control and high integration, making it a glucose monitoring product that provides accurate control within a relatively small size.

Systems, computer programs, devices, and methods that enable coordination across multiple devices of the mobile ecosystem. In one embodiment, smart glasses detect when a user is about to eat food or take a drink and capture the consumable and portion. The data is recorded in a "morsel track" for health activity analysis. Low-fidelity captures provide preliminary recognition, while higher-fidelity captures are selectively invoked for definitive classification. Machine-learning logic generates predicted metabolic responses, such as real-time glucose trends, based on the recorded events. Predicted responses may dynamically adjust the operation of continuous glucose monitors, heart-rate sensors, or other biomedical devices. In some embodiments, the system triggers a pharmaceutical dispenser, such as an insulin pump, inhaler, or transdermal patch, to provide closed-loop therapeutic intervention in real time.

Glucose level measurements of a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. These glucose level measurements can be produced substantially continuously, such that the device may be configured to produce the glucose level measurements at regular or irregular intervals of time, responsive to establishing a communicative coupling with a different device, and so forth. These glucose level measurements are analyzed to detect deviations from past glucose measurements, such as glucose measurements received earlier in the day or glucose measurements received at corresponding times of one or more preceding days. Indications of detected deviations are provided to the user or communicated elsewhere, such as to a healthcare professional.

A glucose monitoring device is provided. The glucose monitoring device includes a housing having a first portion and a second portion, the second portion operable to be placed adjacent to a human body during operation of the glucose monitoring device. The glucose monitoring device further includes a circuit board positioned between the first portion and the second portion of the housing, the circuit board having a trace antenna. The glucose monitoring device further includes a conductive structure spaced apart from the circuit board.

A computer-implemented method for predicting a risk whether a hypoglycemic event will occur during a prediction time interval is disclosed. The method comprises: providing user values for a plurality of machine learning input parameters, the user values determined from glucose monitoring data with glucose measurement values detected for a user before a cut-off time, the plurality of machine learning input parameters comprising: at least one first glucose measurement value determined from glucose measurements values measured within at most 10 minutes before the cut-off time, and a hypoglycemic event share value indicative of a share of previous prediction time intervals with detected hypoglycemic events out of a total number of previous prediction time intervals before the cut-off time; predicting, based on the user values and using a machine learning model, a risk whether a hypoglycemic event will occur during a prediction time interval associated with assumed sleep of the user, the machine learning model generated based on the machine learning input parameters and using historical glucose monitoring data of a user population; and generating an output based on the predicted risk. Further, a data processing system, a computer program, a system, and a remote control are disclosed.

A transdermal glucose monitoring system is provided. The system includes: a continuous glucose monitoring (CGM) device, configured to monitor glucose concentration in subcutaneous interstitial fluid of a patient; a patient user interface, configured to acquire information of the CGM device, receive glucose concentration data, and display a warm-up progress indicator of the CGM device; and a cloud server, configured to complete data sharing and authorize access to data. The provided patient user interface focuses on the warm-up time of the CGM device and provides a positive and intuitive display thereof, along with visual management of glucose monitoring data analysis, thereby enhancing the convenience of glucose monitoring. Furthermore, the system supports a second monitoring device, enabling family and friends to promptly understand the patient's glucose monitoring status and infer the causes of glucose fluctuations.

The invention discloses a 3D nano microneedle electrode for improving glucose monitoring performance and a preparation method and application thereof, and relates to the technical field of biosensing. The 3D nanometer microneedle electrode for improving the glucose monitoring performance comprises a microneedle electrode base material and an electrode sensing material, the microneedle electrode base material is a stainless steel intradermal injection needle, the microneedle electrode base material is filled with a conductive filler, the conductive filler comprises carbon paste and silver paste, and the electrode sensing material comprises an inner layer structure and a surface layer structure. The inner-layer structure is a conductive nano-carrier, and the surface-layer structure is a compound enzyme layer. The invention also provides a preparation method of the 3D nano microneedle electrode for improving the glucose monitoring performance. The utilization rate and catalytic activity of Prussian blue are improved, electron transfer is accelerated, and the problems that a traditional glucose sensor is low in electron transfer efficiency, insufficient in stability and limited in detection range and accuracy are effectively solved.

An application running on a device to manage insulin site rotation may read a configuration file including a group, a plurality of sites in the group, and an image associated with the group. The application may detect an input selecting a site from the plurality of sites for administration of an insulin management device. The application may also set the selected site as unavailable for a predetermined duration in response to the input selecting the site. A log entry may be written to track usage of the site in response to the input selecting the site. A visual indicator on the selected site may show that the site is recently selected in response to detecting the input selecting the site within a predetermined duration. The insulin management device may be a pump, a syringe, or a glucose monitor.

Electrochemical impedance spectroscopy (EIS) is used in conjunction with continuous glucose monitors and continuous glucose monitoring (CGM) to enable in-vivo sensor calibration, total (sensor) failure analysis, and intelligent sensor diagnostics and failure detection. An equivalent circuit model is defined and each circuit element is used to characterize sensor behavior.

Methods, systems, and devices for evaluating a user's metabolic balance are described. A system may include a wearable device and a glucose monitoring device. The system may acquire physiological data from the wearable device, and blood glucose data from the glucose monitoring device. The system may be configured to determine physiological characteristics of the user based on the physiological data, such characteristics associated with a sleep quality of the user, a stress level of the user, an activity level of the user, a recovery of the user, or any combination thereof. Similarly, the system may identify metabolic characteristics of the user, such as changes in the user's blood glucose data. By overlaying and comparing the physiological data and the blood glucose data, the system may be able to evaluate how the user's physiological characteristics affect or impact the user's metabolic characteristics, or vice versa.

A compensation method for a continuous glucose monitor includes: acquiring a glucose value of a user continuously; determining a user type and a glucose trend type according to the glucose value, where the user type includes at least a first user type and a second user type; determining, based on the glucose value, whether a compensation start condition is satisfied;starting a compensation algorithm if the compensation start condition is satisfied; determining a compensation formula based on the glucose value and the glucose trend type; and obtaining, based on the glucose value and the compensation formula, a compensated glucose value until a compensation stop condition is satisfied.

The invention discloses an analysis model as well as a system and a method for applying the analysis model to multifunctional intelligent shoes for diabetes. The system comprises an intelligent shoe body, an intelligent terminal and a dynamic blood glucose monitor, the intelligent shoe body comprises a shoe body, a sole and an insole; the upper surface of the insole is divided into a plurality of sensing areas, and a pressure sensing module is arranged in each sensing area; a functional area is fixedly arranged on the shoe body, and a wireless module is installed in the functional area; the wireless module is electrically connected with each pressure sensing module through a connecting wire, and is wirelessly connected with the intelligent terminal and the dynamic blood glucose monitor. The intelligent terminal is provided with a blood glucose abnormal trend analysis model based on plantar pressure distribution, a long-short memory model is trained through subarea pressure distribution data according to different age groups, suspected and definite case data are analyzed from a test set, and therefore a distance model for distance definite diagnosis is constructed, and the function of early prevention of diseases is achieved.

Described herein are examples of systems and methods for determining the efficacy of a therapeutic intervention related to the treatment of diabetes. In an example, a continuous glucose monitor can be used to monitor the glucose levels of a subject. An insulin pump can administer insulin to the subject in response to the glucose levels obtained by the continuous glucose monitor. The efficacy of a treatment intervention can be determined by comparing an amount of insulin administered to the subject by the insulin pump over a timeframe associated with the treatment intervention to an amount of insulin administered to the subject by the insulin pump over a timeframe associated with a control or placebo period and / or a timeframe associated with a second treatment intervention.

The disclosed embodiments are directed to methods for dynamically adjusting the total daily insulin requirements of a user during pregnancy, based on the gestational week. An initial estimate of the adjusted total daily insulin requirement may be calculated as a multiple of the pre-pregnancy total daily insulin requirement, based on an average scale factor from a population of pregnant women suffering from Type I diabetes mellitus. An automatic drug delivery device may adjust the initial estimate of the total daily insulin requirement based on blood glucose level readings from a continuous glucose monitor during the course of the pregnancy.

Diabetes management systems may include a glucose monitoring device adapted to transmit glucose data and at least one accessory in communication with the glucose monitoring device and adapted to provide at least one alarm or alert based on the glucose data.

1. The name of the design product: dynamic blood glucose meter reinforced paste. 2. The use of the design product: the design product is used to paste the sensor of the dynamic blood glucose meter around the periphery, and covers part of the sensor to fix it on the skin surface. 3. The design points of the design product: the overall shape of the design product. 4. The picture or photo that best indicates the design points: use state figure 4. 5. The design product is a thin product, and the left view, right view, top view and bottom view are omitted.

Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, an observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data for a population of users to predict a diabetes classification for an individual user. The historical glucose measurements for the population of users can be provided by glucose monitoring devices worn by users of the population of users, while the historical outcome data includes one or more diagnostic measurements obtained from a source independent of the glucose monitoring devices. After training is complete, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device over an observation period spanning multiple days. The predicted diabetes classification can then be output, such as by generating one or more notifications or user interfaces based on the classification.

The present invention is a glucose monitoring device designed specifically for deaf individuals. The device comprises a housing containing components such as glucose monitoring circuitry for measuring glucose levels in blood samples. The front surface features a display screen that shows glucose readings and other relevant data. The housing is equipped with multiple bright flashing lights and a vibration motor to provide both visual and tactile alerts when glucose levels are abnormal. The alerts persist until acknowledged by pressing an acknowledgement button. Additionally, the device includes a pivotal stand on the rear surface, enabling the device to be angled for optimal visibility of alerts. The system also incorporates a wireless communication channel for transmitting real-time glucose measurements to a remote computing device, facilitating monitoring by users, guardians, or medical practitioners.

Systems, computer programs, devices, and methods that enable coordination across multiple devices of the mobile ecosystem. In one embodiment, smart glasses detect when a user is about to eat food or take a drink and capture the consumable and portion. The data is recorded in a “morsel track” for health activity analysis. Low-fidelity captures provide preliminary recognition, while higher-fidelity captures are selectively invoked for definitive classification. Machine-learning logic generates predicted metabolic responses, such as real-time glucose trends, based on the recorded events. Predicted responses may dynamically adjust the operation of continuous glucose monitors, heart-rate sensors, or other biomedical devices. In some embodiments, the system triggers a pharmaceutical dispenser, such as an insulin pump, inhaler, or transdermal patch, to provide closed-loop therapeutic intervention in real time.

Methods, systems, and devices for evaluating a user's metabolic balance are described. A system may include a wearable device and a glucose monitoring device. The system may acquire physiological data from the wearable device, and blood glucose data from the glucose monitoring device. The system may be configured to determine physiological characteristics of the user based on the physiological data, such characteristics associated with a sleep quality of the user, a stress level of the user, an activity level of the user, a recovery of the user, or any combination thereof. Similarly, the system may identify metabolic characteristics of the user, such as changes in the user's blood glucose data. By overlaying and comparing the physiological data and the blood glucose data, the system may be able to evaluate how the user's physiological characteristics affect or impact the user's metabolic characteristics, or vice versa.

A method of therapy upgrade for a diabetic patient includes: receiving glucose data of a user from an in vivo glucose monitoring device; receiving first therapy information for a first therapy, wherein the first therapy includes basal insulin; calculating one or more glucose metrics based on the received glucose data; titrating a dose of basal insulin based on the one or more glucose metrics; and determining a basal insulin excess based on one or more of the glucose data and the first therapy information. Advantageously, the system is capable of regularly monitoring a user's glucose control, detecting basal insulin excess, providing frequent therapy intervention and adjustments, reducing the duration of intervention, and improving user adherence, efficacy, and satisfaction.

A system is provided comprising external device(s), tracking device(s) configured to generate blood glucose level data for tracked individual(s), and an alert device for management of alerts generated at the external device(s). The alert device comprises processor(s) and memory device(s). The memory device(s) comprise computer readable code that, when executed by the processor(s), causes the processor(s) to receive input data comprising blood glucose level data received from the tracking device(s), to determine an alert setting for an alert to be generated at an external device of the external device(s) based on the input data, and to generate a signal that causes the alert to be generated at the external device. The alert setting includes an identification of the external device where the alert is to be generated, an alert magnitude, and / or the alert type, with the alert type including an audible alert, a haptic alert, and / or a visual alert.

Embodiments include methods and systems for regulating a user's glucose levels. Systems comprise a processing unit in communication with a glucose monitor, an accelerometer, and a user interface. The glucose monitor generates glucose readings corresponding to the user's glucose levels and the accelerometer generates acceleration readings corresponding to the user's activity. The processing unit determines if the glucose readings indicate a glucose state and whether the acceleration readings indicate an activity state. If the glucose state and the activity state coincide, the processing unit sends a message to the user interface recommending a behavior that would influence the user's glucose level. Methods comprise determining, at a processing unit, if glucose readings indicate a glucose state, if acceleration readings indicate an activity state, whether the glucose state and activity state coincide and, if so, sending a message to a user interface recommending a behavior that would influence the user's glucose level.

A portable infusion pump can communicate with glucose monitor, such as a continuous glucose monitor (CGM), to receive continuous feedback relating to a user's blood glucose level during insulin or other medicament therapy and can automatically deliver insulin to a user when the CGM data indicates a need for additional insulin. Due to potential unreliability in the correlation of the CGM data to the user's actual blood glucose level, risk mitigation can be employed to limit the amount of extra insulin that can be delivered by the pump in response to the CGM data.

Systems, devices and methods are provided for incorporating a medication delivery device into an integrated management system. The integrated management system may be an integrated diabetes management system and may include a glucose monitor, a connected insulin pen, and software. The integrated management system may produce a plurality of reports that may include data related to analyte levels (e.g., glucose levels) and medication delivered (e.g., insulin delivered). The integrated system may also include a mode in which certain types of data are no longer shared and / or stored if the user is not signed into an account. The types of data shared and / or stored when the user is not signed into an account may differ from the types of data shared and / or stored when the user is signed into an account.

A system for continuous glucose measurement and spinal cord stimulation provides at least two implantable non-stimulation electrodes configured to continuously monitor at least one physiological parameter on a lead that also includes a plurality of implantable stimulation electrodes. The electrodes are electrically connected to an implantable pulse generator that communicates stimulation instructions and glucose measurement data.

Glucose level measurements of a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. These glucose level measurements can be produced substantially continuously, such that the device may be configured to produce the glucose level measurements at regular or irregular intervals of time, responsive to establishing a communicative coupling with a different device, and so forth. These glucose level measurements are analyzed to detect deviations from past glucose measurements, such as glucose measurements received earlier in the day or glucose measurements received at corresponding times of one or more preceding days. Indications of detected deviations are provided to the user or communicated elsewhere, such as to a healthcare professional.

An assembly for securing a glucose monitoring device and related techniques are disclosed. The assembly may include a retainer member configured to fit loosely over the glucose monitoring device without attaching or connecting to it. To that end, the retainer member and a housing of the glucose monitoring device may include respective interfacing features configured to interface with one another. The assembly further may include an adhesive overpatch configured to adhere to the retainer member and the user's skin without adhering to the glucose monitoring device. In accordance with some embodiments, removal of the adhesive overpatch from the user's skin may effectuate simultaneous removal of the retainer member from the glucose monitoring device without compromising adhesion of the glucose monitoring device to the user's skin or disrupting placement of its implanted sensor.

A computer-implemented method for predicting a risk whether a hypoglycemic event will occur during a prediction time interval is disclosed. The method comprises: providing user values for a plurality of machine learning input parameters, the user values determined from glucose monitoring data with glucose measurement values detected for a user before a cut-off time, the plurality of machine learning input parameters comprising: at least one first glucose measurement value determined from glucose measurements values measured within at most 10 minutes before the cut-off time, and a hypoglycemic event share value indicative of a share of previous prediction time intervals with detected hypoglycemic events out of a total number of previous prediction time intervals before the cut-off time; predicting, based on the user values and using a machine learning model, a risk whether a hypoglycemic event will occur during a prediction time interval associated with assumed sleep of the user, the machine learning model generated based on the machine learning input parameters and using historical glucose monitoring data of a user population; and generating an output based on the predicted risk. Further, a data processing system, a computer program, a system, and a remote control are disclosed.