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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Intelligent real-time dynamic blood glucose monitoring system using cloud big data to improve accuracy and overcome limitations of implantable glucose sensors. The system leverages historical blood glucose data from a user to correct sensor signals in real-time. It accesses the user's stored data from a cloud server and uses algorithms to establish regression equations relating sensor signals, impedance, and historical glucose levels. This compensates and modifies the calculated blood glucose results from the sensor to improve accuracy by accounting for individual variations and sensor artifacts.

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A wearable patch for monitoring blood glucose levels of diabetes patients using machine learning. The patch has sensors like PPG and NIR to detect blood circulation and glucose levels. The sensor data is analyzed by a microcontroller and then sent to a cloud server for further analysis using machine learning. If the glucose level gets abnormal, an alarm on the patch notifies the patient to take action. The patch aims to provide non-invasive, remote, and continuous glucose monitoring without finger pricks.

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A wearable continuous glucose monitoring system using LoT (Internet of Things) and Fog computing that enables remote and continuous glucose monitoring for patients. The system has a wearable sensor device that collects glucose and body temperature data. The sensor communicates wirelessly to a nearby gateway device, like a smartphone, which relays the data to the cloud. The Fog computing layer between the sensor and cloud provides local processing and storage to reduce data transmission and improve reliability. The system allows remote monitoring of glucose levels by doctors and caregivers through a web or mobile app. The sensor has integrated power management and energy harvesting to enable long-term wearability.

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System and method for processing and transmitting sensor data like glucose levels from implantable sensors to external devices. The sensor communicates intermittently to conserve power. It activates the transceiver, establishes a secure channel, sends data, and deactivates. The external device authenticates and requests data only during designated time windows. This reduces unnecessary transmissions. The sensor also periodically measures and stores data for later retrieval.

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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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A remote blood glucose monitoring system that enables accurate, painless blood glucose monitoring without the need for frequent hospital visits. The system uses a micro-implantable glucose sensor that is implanted under the skin and connected wirelessly to a portable device. The implanted sensor collects glucose levels over time and sends the data wirelessly to the device for analysis. The device displays the results, stores them, and sends them to a central database. This allows real-time monitoring, storage, and sharing of glucose levels without frequent hospital visits. The implanted sensor reduces pain compared to traditional finger pricking and the wireless transmission enables remote monitoring.

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A blood glucose monitoring system and method for diabetes management that uses cloud computing to provide personalized feedback and guidance based on remote glucose readings. The system involves a non-invasive glucose meter that connects to a cloud platform to transmit readings. The platform analyzes the data and provides customized advice to the user based on factors like user type (diabetic, pregnant, normal), test frequency, and historical trends. It can also prompt users to seek medical attention if readings are abnormal. This enables remote monitoring and guidance without intensive medical resources.

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A system for real-time blood glucose monitoring and early warning for multiple patients using a networked setup with a central cloud server, multi-channel clinical blood glucose data acquisition device, and mobile terminals. The system allows doctors to monitor and analyze blood glucose levels of multiple patients in real-time, provide early warnings of abnormal glucose trends, and optimize insulin dosing based on predictions. The central cloud server collects and processes the patient data from the multi-channel clinical device, which can include continuous glucose monitors and insulin infusion sensors. The mobile terminals allow doctors to access and view the patient data remotely. This allows doctors to see the blood glucose data of multiple patients simultaneously, which can improve their ability to manage diabetes care for multiple patients in a clinic setting.

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A diabetes monitoring system that leverages continuous glucose monitoring to accurately assess and manage blood glucose control for people with type 2 diabetes. The system involves a patient wearable device that continuously measures glucose levels, a local workstation for data analysis, and a remote server for centralized analysis. The system uses predefined thresholds to diagnose blood glucose control based on factors like average, variability, and hypoglycemia. This provides a more accurate and efficient way to monitor diabetes versus regular hospital visits. The system also allows population-wide analysis using aggregated glucose data to better understand diabetes management.

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A diabetes remote monitoring device that can connect and transmit data from multiple types of glucose meters using interoperable protocols. The device has modules for connecting to glucose meters and the internet. It can identify the type of glucose meter connected and use the appropriate protocol to communicate with it. It also has a motion sensor to activate the device when moved. This allows using a single device to monitor from different glucose meters without needing separate apps or adapters for each meter.

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An analyte monitoring system that allows sharing of patient's analyte data with remote observers. The system has a primary display device with a sensor, calculates analyte levels, and sends data over a network. Secondary devices receive invitations to access the data, accept, and view patient analytes remotely. It enables others to monitor analytes of a patient using their own devices.

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A wearable device for continuous glucose monitoring that can detect hypoglycemia and coma and provide self-help and alerts. The device is a separate transmitter worn alongside the glucose sensor. It has a blood glucose sensor, accelerometer, wireless SoC, and motorized alarm. The sensor measures glucose and sends data to a phone app. The accelerometer detects motion and sleep. If glucose drops dangerously, the device alarms with the motor and sends an alert to the phone. If the person is unresponsive, the accelerometer can indicate coma. The phone app provides remote monitoring, stats, and alerts.

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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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A wearable health monitoring system that continuously tracks vital signs and provides personalized insights and recommendations based on individual physiology and lifestyle. The system uses wearable sensors, a portable device, and cloud analytics to monitor vital parameters like blood pressure, heart rate, oxygen saturation, respiratory rate, glucose, temperature, and activity. It extracts the vital signs using embedded algorithms and analyzes them over time to detect health conditions and predict future ones. The system adapts the monitoring to the user's unique physique and lifestyle. It also provides tailored nutrition and wellness programs.

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Wearable non-invasive blood glucose monitor that allows continuous real-time monitoring of blood sugar levels without the need for finger pricking. The monitor uses a non-invasive sensor to detect glucose levels in the skin. It has wireless communication capabilities to transmit data to a mobile device. This enables all-weather monitoring and analysis of blood sugar using cloud computing and mobile internet services.

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Wearable blood glucose monitoring system that provides reliable, continuous glucose monitoring without the need for blood samples. The system uses a wearable device with a sweat glucose sensor, body temperature sensor, display, and communication components. The sensor adheres to the skin and transmits sweat glucose measurements to the wearable device. The device displays glucose and body temperature, sends data to a mobile app, and performs predictive modeling to anticipate blood glucose levels. This allows proactive alerts and interventions before blood glucose spikes or dips. The wearable also has a metformin injection component triggered by high glucose levels.

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Advanced medical system for managing health conditions like diabetes that uses sensors, actuators, logic circuits, and communication schemes to monitor and treat patients. The system includes devices like contact lenses, drones, and wearables with embedded sensors and actuators. It also has features like energy management, cryptography, social mechanisms, and personalized sensors. The system can involve devices like glucose sensors, insulin pumps, CGM, FGM, spectrometers, etc.

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Method for remote monitoring and managing blood glucose levels for diabetic patients using a mobile device. The method involves analyzing historical blood glucose data from a non-invasive glucose device to determine when the user is likely to have abnormal blood sugar. The mobile device then increases monitoring frequency of the non-invasive device within a set time period around the predicted abnormal time. This allows proactive monitoring and intervention to prevent hypo or hyperglycemia. The mobile device can also instruct the non-invasive device to administer insulin or glucose based on current readings.

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Wearable non-invasive blood glucose monitor that allows continuous, real-time glucose monitoring without the need for finger pricking. The device uses a non-invasive monitoring technique to measure glucose levels from the skin. It wirelessly communicates with a mobile device to provide glucose readings and analysis. This enables wearable blood glucose monitoring that is more convenient and less painful than traditional finger pricking methods. The device leverages mobile internet and cloud computing to provide users with blood glucose monitoring, data analysis, and health advice.

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Enhancing continuous glucose monitoring (CGM) using smartphones and other devices to provide more informative feedback to diabetics. The method involves leveraging features of smartphones, tablet computers, etc. to process combined data from CGM sensors and other applications to provide outputs like alerts, recommendations, and scheduling that are more informative than sensor data alone. This can help diabetics make better decisions around insulin therapy, diet, exercise, etc. by providing context and insights based on sensor data, user input, and other factors.

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Continuous analyte monitoring system for real-time, continuous tracking of analytes like glucose levels in the body. The system uses a sensor implanted in the body to measure analyte concentrations and a transceiver to wirelessly transmit the data to a display device like a smartphone. The transceiver also receives power to operate the sensor. The display device calculates analyte concentrations and trends, and can upload the data to a management system. The system allows convenient, real-time monitoring without frequent finger sticks.

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