Wearable Sensor Feedback for Dynamic Coronary Flow Modeling
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Solution Overview
Problem
Existing personalized anatomical and physiological models for coronary artery disease do not account for user-specific activity data, leading to inaccuracies as user characteristics change over time, and lack real-time input, resulting in diagnostic results that may lose relevance.
Innovation Solution
A system and method for calculating blood flow metrics using user-specific anatomical models and sensor data, including activity data from sensors such as accelerometers and heart rate monitors, to continuously update blood flow simulations and predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personalized anatomical and physiological models are used for blood flow simulation, then diagnostic accuracy is improved, but the models become outdated as user characteristics change over time
Solution Approach 1:
The patent transforms static anatomical and physiological models into dynamic models that continuously adapt to user changes. Sensor data from wearables (heart rate, activity level, blood pressure) feeds into the simulation model, allowing it to update blood flow predictions in real-time as the user's physical condition evolves, thereby maintaining diagnostic accuracy without requiring repeated invasive procedures.
Solution Approach 2:
The system implements a feedback loop where sensor measurements from the user are continuously incorporated into the blood flow simulation model. This feedback mechanism allows the model to self-correct and update its predictions based on actual user data, ensuring the model remains valid and accurate over time despite changes in the user's anatomy or physiology.
2Reliability
If real-time sensor data is incorporated into blood flow simulations, then diagnostic relevance is improved, but system complexity increases
Solution Approach 1:
The patent leverages multi-functional wearable sensors that simultaneously track multiple physiological parameters (heart rate, activity level, blood pressure, oxygen saturation) using a single device. This universal approach reduces the need for multiple specialized sensors and simplifies the data acquisition system while providing comprehensive input for the blood flow simulation model.
Solution Approach 2:
The system introduces a computational intermediary layer that processes raw sensor data and translates it into meaningful physiological parameters for the blood flow model. This intermediary processing layer abstracts the complexity of sensor integration and data fusion, making the system more manageable while maintaining diagnostic relevance through continuous real-time updates.
Data Source
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AI summary
Systems and methods are disclosed for assessing health state of a user. One method includes obtaining medical data that includes a model representing a user's anatomy; determining an initial health state of the user, at an initial point in time, based on the model; receiving sensor data from a wearable device associated with the user, wherein the sensor data is indicative of a physiological state of the user; generating an updated health state of the user by updating the health state of the user over a period of time between the initial point and a time after the initial point based on the sensor data; determining a change in the health state of the user over the period of time; projecting a change in the health state of the user based on the change in the health state of the user over the period of time.