Cloud-Connected Glucose Monitors for Remote Health Tracking

A real-time, continuous glucose monitoring system for diabetes management that uses a microcontroller, glucose sensor, and Wi-Fi module to transmit glucose data to the cloud. The system allows individuals with diabetes to track their glucose levels in real-time and in real flow, improving the reliability and accuracy of the data compared to fingerstick testing. The system can also provide tailored healthcare solutions by allowing healthcare practitioners to access the real-time glucose data to improve treatment regimens. The use of the Internet of Things (IoT) and cloud computing enables continuous glucose monitoring without the need for frequent fingerstick tests.

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Instantaneous dynamic blood glucose monitor with an alarm function to improve the timeliness of glucose level alerts. The monitor has a sensor with a detection module to collect blood glucose data and a communication module to send it to a terminal. The terminal has a storage module, processing module, and separate alarm module. This allows the sensor to continuously send real-time glucose readings to the terminal which can process and analyze them. If a critical glucose level is detected, the alarm module can immediately trigger an alert without relying on the user to check the monitor.

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Remote monitoring system for diabetic patients that uses a communication system to collect physiological measurements from devices on the patient. A neural network analyzes the time series data to detect risks of hyperglycemia or hypoglycemia. If an alert is generated, it notifies the patient and/or offers insulin/carbohydrate recommendations to prevent blood sugar issues. The system allows remote monitoring of diabetic patients to detect and address potential glucose extremes outside of medical settings.

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Enabling secure and convenient transfer of glucose monitoring data to external systems like EMRs without requiring a wired connection or user account setup. The glucose monitoring system receives a request from an external system like an EMR to establish a connection for data sharing. It compares patient identifiers to confirm the requesting patient has glucose monitoring data. If so, it generates an association and sends a confirmation. If not, it denies the request. This allows external systems to request and receive glucose data without direct device connectivity or user account setup.

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Blood sugar management system that provides secure, scalable, and real-time blood glucose monitoring for artificial pancreas devices. The system uses a dedicated portable device for blood sugar management that communicates with a glucose monitor and insulin pump. The device has a processor, memory, and wireless modules for local glucose sensing, insulin dosing, and public network connectivity. This allows updating algorithms, receiving external data, and transmitting glucose/insulin info. The dedicated device provides secure, continuous, and scalable blood sugar management compared to smartphone apps or built-in pump algorithms.

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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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Enabling remote monitoring of health data like glucose levels from continuous sensors by allowing authorized users to access and view the data from a separate device. The system uses a primary sensor on the person being monitored, like a continuous glucose monitor, to send data to a server. Remote monitors, like family members or caregivers, receive notifications when events like low glucose are detected. They can then log in to the server to view the full sensor data and settings. This allows remote monitoring without needing separate sensors on each person.

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Ecosystem that enables interconnection and data exchange between a blood glucose monitor and a continuous glucose monitor to provide improved glucose monitoring for diabetes management. The blood glucose monitor can request measurements from the continuous glucose monitor when discrepancies exceed thresholds. This allows cross-validation and verification of readings. The continuous glucose monitor can also generate alerts based on its data that are output by the blood glucose monitor. This provides suggestions for actions to take at times of high glucose levels based on trends identified from the continuous monitoring.

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Securely transmitting and managing glucose monitoring data from continuous glucose monitors to enable selective access for different purposes while protecting patient privacy. The data is classified into restricted and less-restricted categories based on sensitivity. Devices transmit data to a cloud infrastructure, which stores it separately. Applications request access to specific categories. This allows controlling who can view glucose levels versus diagnostics without revealing patient identity.

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A machine learning blood sugar monitoring system using a continuous glucose monitoring (CGM) sensor that provides real-time, personalized blood sugar monitoring without frequent finger pricks. The system involves a patient app, CGM sensor, medical app, and server. The patient app collects CGM data, sets state info (meds, diet, exercise), and does OGTT tests. The app interacts with the server to process, analyze, and display the data. Unsupervised learning is used to extract features from the preprocessed CGM and state data. This allows tracking of integrated blood sugar trends, medication, diet, and exercise impact.

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A medical blood glucose monitoring system that enables more effective management and analysis of continuous glucose monitoring (CGM) data for diabetes patients. The system involves a sensor, transmitter, data receiving terminal, cloud server, and medical monitoring terminal. The sensor and transmitter communicate to send CGM data wirelessly to the terminal. The terminal uploads the data to the cloud, where it's correlated with user-inputted events like medication, diet, exercise, etc. The cloud then returns the event-associated CGM data to the terminal for display. This allows visualizing glucose trends with events overlaid. The system also calculates metrics like A1C and daily glucose variability.

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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 blood glucose monitoring system that accurately and reliably measures and displays blood glucose levels using a continuous glucose monitor (CGM) worn on the body. The system uses algorithms to process and analyze the CGM data to improve accuracy and reduce delays. The algorithms include abnormal data exclusion, initial wear calibration, reference correction, segmentation, and prediction. The system also uses Bluetooth to connect the CGM and a local terminal, and WiFi to sync with a cloud server, providing stable data transmission regardless of network conditions.

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System for monitoring, analyzing, and providing feedback on glucose levels to help prevent and manage diabetes. The system uses non-invasive sensors like glucose, stress, activity, sleep, and food intake sensors to continuously monitor subjects. It detects glucose anomalies, analyzes the context like stress, activity, sleep, and food, and maps the subject's position on a glucose condition graph. This provides personalized feedback on lifestyle changes to improve glucose levels. The system uses AI to cluster subjects and forecast changes.

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A system for interpreting and managing bioanalyte measurements from devices like glucose sensors to provide useful information to users. The system involves collecting data from bioanalyte monitoring devices, interpreting it to account for delays between interstitial and capillary measurements, and sending the interpreted data to external devices with specialized software to provide actionable insights to users. This allows more accurate and clinically relevant interpretation of interstitial measurements compared to calibrated capillary measurements.

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Blood glucose monitoring system for diabetic patients with customizable reference correction frequency to improve accuracy and user experience. The system allows users to choose the reference correction frequency based on their specific condition. This flexibility ensures the user's blood glucose level stays within normal range without excessive corrections. The system has a sensor, transmitter, receiving terminal, and cloud server. The sensor measures glucose levels and transmits to the terminal. The terminal processes and sends to the cloud for analysis. The system also has a condition selection module for users to choose between medical and home monitoring versions. The versions have different reference input prompts. The reference correction algorithm compares sensor readings to a reference value. The reference correction frequency algorithm adjusts the frequency based on user selection and deviation data.

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Monitoring and treating health conditions using wearable devices and interoperable data standards. The system involves using wearable sensors to continuously monitor biomarkers like glucose, heart rate, etc. The sensors communicate over wireless networks to a central database. The database normalizes and stores the data using common formats. This allows interoperability between devices from different manufacturers. It also enables sharing of normalized data with other systems like emergency vehicles or hospitals. The normalized data can be analyzed to detect medical issues like stroke or glucose extremes. This allows proactive treatment before symptoms appear.

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Analyte monitoring system with improved communications between sensor electronics units and display devices for continuous glucose monitoring (CGM). The system uses a communication protocol that allows efficient, power-saving data transfer between the sensor and display. It avoids repetitive pairing and synchronization procedures that consume resources. The protocol has features like forced wakeup, on-demand pairing, and extended warning signals to improve ease of use and security. It enables extracting sensor data when the unit has no power or failed.

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Blood glucose monitoring system using a smartwatch strap that enables continuous, wireless blood glucose monitoring without the need for a separate meter. The system includes a wearable device with a display, speaker, and wireless connectivity, along with a tiny implantable glucose sensor. The sensor is attached to the skin using a strap that connects to the watch. It wirelessly transmits glucose data to the watch for display, alerts, and storage. The watch can then send the data to a cloud server for remote viewing by doctors and caregivers. The sensor is detachable for replacement.

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