Combining Multiple Sensing Modalities for Glucose Monitoring

System to augment analyte monitoring by leveraging a separate wearable device that can measure additional physiological signals. The primary analyte monitoring device collects analyte data like glucose. A separate wearable with additional sensors contacts the skin around the primary device and communicates its data. Both wearables send their data to a central hub. This allows correlation and augmentation of analyte measurements with other physiological signals.

A device for measuring blood pressure that accommodates changes in applied pressure during a heartbeat by combining features from occlusion, pulse wave velocity (PWV), and pulse wave analysis (PWA) devices. The device measures arterial area changes during a heartbeat, applies pressure to the body part, and measures instantaneous applied pressure. It finds blood pressure by analyzing area vs. applied pressure. This compensates for pressure fluctuations during a heartbeat to make accurate measurements.

Closed-loop insulin infusion systems using orthogonally redundant glucose sensors for improved accuracy and reliability. The system has two glucose sensors, one optical and one electrochemical, to provide orthogonal redundancy. An algorithm combines the sensor data to improve accuracy and reliability. If one sensor fails, the other can provide glucose values. The sensors have features like distributed electrodes and membrane barriers to reduce drift and fouling. The system uses on-demand calibration rather than frequent fingersticks.

Wearable health monitoring devices that can simultaneously measure electrocardiogram (ECG) and bioimpedance (BI) using just one or two pairs of shared electrodes. This allows smaller, more inconspicuous wearables with fewer electrodes than traditional systems. The shared electrodes are used to extract ECG and BI measurements from a single sensed signal. An injection current is provided through one set of electrodes while the other set is used for sensing. This enables ECG and BI measurements without additional electrodes, reducing size and weight.

Mobile case for smartphones that enables non-intrusive physiological monitoring using integrated sensors. The case has recesses with optical sensors and electrodes for bioimpedance measurements. This allows continuous, touchless monitoring of parameters like body composition, hydration, heart health, and glucose levels by leveraging the phone's existing touchscreen. The case captures daily data for improved reliability and uses machine learning to filter out noise. Statistical analysis detects trends for health insights.

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.

A single-probe, multi-analyte continuous glucose monitoring system for improved automated insulin delivery in diabetes management. The system has a minimally invasive probe that measures glucose and at least one other analyte like lactate or ketones. Integrating multiple analytes and optional physical sensors into the probe, it provides contextual information about physiological states like meals, exercise, stress, and sleep. This seamless data stream from a single probe reduces the burden compared to multiple device insertions. The system uses the combined signals to refine personalized insulin delivery and artificial pancreas algorithms for closed-loop control. It enables 24/7 automation without needing separate devices for each analyte.

Reducing the error in continuous glucose monitoring (CGM) devices by improving the prediction of blood glucose levels. The method involves using a sensor to measure blood flow and heart rate, along with a glucose sensor for interstitial fluid. The device determines the time it takes glucose to diffuse from blood to interstitial fluid based on the blood flow and heart rate signals. It then uses this adjusted time to more accurately predict blood glucose levels from the interstitial fluid measurement.

A wearable diagnostic device that can perform multiple medical diagnostic tests non-invasively and continuously. The device has sensors to measure parameters like heart rate, hemoglobin, temperature, oxygen, glucose, cholesterol, and blood pressure. It can also detect conditions like Parkinson's symptoms. The device activates the sensors based on requested tests, compares measured signals to predicted values, and outputs the test results. It can also intelligently select and sequence tests based on user profiles and histories.

A compact, non-invasive health monitoring device that can continuously track vital signs, blood chemistry, sleep, and other parameters without needing blood samples. The device is a wearable biosensor that attaches to the skin and uses photoplethysmography (PPG) to measure blood flow and concentrations of substances like oxygen, glucose, nitric oxide, and alcohol. The biosensor has a memory to store patient ID and medical records, and wireless transmission to send data to medical systems. It aims to provide continuous, non-invasive monitoring and diagnosis without needing blood draws or scanning.

A wearable device for people with type 2 diabetes to help them regulate their blood glucose levels through real-time feedback on physical activity. The device has sensors to monitor glucose levels and movement. It compares current and historical readings to determine the relationship between glucose and activity. If glucose levels fit a certain profile, it sends signals to the user to engage in physical activity. The device aims to reinforce the impact of physical activity on glucose control and provide immediate prompts to modulate glucose levels. It also stores historical data for comparison.