Innovations in Glucose Monitoring Devices that Send Data to the Cloud

A real-time dynamic blood glucose monitoring system leverages cloud big data to enhance the accuracy of implantable glucose sensors by considering individual differences. The system collects historical glucose measurements from a user's finger meter and cloud server. It employs machine learning algorithms to establish a regression equation that links sensor signals, impedance, and historical glucose values. This approach enables the system to compensate for variations and adjust the sensor output to align with the user's specific glucose response.

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An in vivo glucose monitoring system provides critical alerts for hypoglycemia and impending hypoglycemia while keeping communication dormant otherwise. The system features a transcutaneous glucose sensor with electronics that continuously monitor glucose levels. It detects hypoglycemic conditions based on glucose trends and generates alerts. Rather than constantly transmitting glucose data, the system waits for a request from a reader device. When an alert is active, a separate RF module is activated to send the alert specifically. This approach conserves power and communication bandwidth. Additionally, each alert reduces the sensor's lifespan by a fixed amount.

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A continuous glucose monitoring system combines invasive and non-invasive measurements to deliver more accurate and continuous glucose level data. The system simultaneously takes an invasive blood glucose measurement and a non-invasive interstitial fluid glucose measurement. It then analyzes data from both sources to provide more reliable glucose estimates than using either method alone. The invasive measurement sets an initial baseline, while non-invasive measurements track changes. This approach helps address issues like time delay and hydration variability that affect non-invasive monitoring. Additionally, the system uses machine learning to predict future glucose levels based on historical data and user activity.

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Handheld medical device for wireless glucose monitoring that improves patient compliance and treatment by facilitating real-time communication. The device measures blood glucose levels using a sensor and wirelessly transmits the readings to a server. The server can then send personalized messages back to the device based on the readings. This provides real-time feedback to the patient and enables remote monitoring and management of their condition. The device also mitigates interference and heat effects on sensors to ensure accurate readings.

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Cloud-based non-invasive blood glucose monitoring system that allows continuous, non-invasive measurement of blood glucose levels without requiring blood sampling. The system uses a device that generates a stimulating signal for the user to contact, which is transmitted wirelessly to a mobile device and then to a cloud server. The server calculates the blood glucose level based on the signal and returns the result to the mobile device. This allows users to monitor their glucose levels without the risk of infection or skin punctures associated with traditional blood testing.

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A portable, handheld system for detecting, monitoring and predictively modeling analytes like pathogens in real-time. The system has a portable detector with probes for multiple analytes, wireless transmission to a mobile device, and cloud processing for quantification, monitoring, and modeling. It allows rapid, on-site detection and monitoring of analytes like pathogens in samples like air, surfaces, or fluids. Real-time data is transmitted to a mobile device for display and cloud analysis. This enables rapid, on-site detection, quantification, monitoring, and modeling of analytes like pathogens in samples like air, surfaces, or fluids. It allows rapid, on-site detection, quantification, monitoring, and modeling of analytes like pathogens in samples like air, surfaces, or fluids.

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Remote healthcare authorization system for patients with chronic conditions that allows remote monitoring and diagnosis without in-person visits. The system involves a patient device that collects physiological data like blood glucose levels, sends it to a remote server, and requests authorization from a remote healthcare provider. The server identifies the patient profile and sends the request with the data to the provider. If authorization is granted, the server notifies the patient device. This enables remote monitoring, diagnosis, and authorization decisions for chronic conditions without requiring in-person visits.

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Controlling the distribution and use of data having multiple categories of sensitivity throughout a distributed architecture like a continuous glucose monitor system. The architecture allows data to be sent from a medical device to connected displays and a cloud system, with the cloud providing selective access based on sensitivity. It prevents unauthorized entities from accessing restricted data while allowing less sensitive data. This involves identifying missing data points, backfilling them when requested within a defined time, and indicating backfilled data. It allows scaling to handle large amounts of data.

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System to securely collect, analyze, and report continuous glucose monitoring (CGM) data from multiple monitors. The system involves a distributed architecture with CGM devices, display devices, cloud servers, and an analysis engine. The data is classified by sensitivity and selectively transmitted through the architecture to control access to restricted data like patient IDs. Encryption and common APIs are used.

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Remote monitoring of health data from a host device like a glucose sensor enables caregivers to view and receive alerts about the host's health condition. The host device sends data to a server, which then pushes notifications to designated remote monitoring devices like smartphones. This allows caregivers to remotely access and monitor the host's health without direct access to the host's device. The remote monitors can customize alert settings and receive additional data from the server.

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