Cuff-less Blood Pressure Scanner Adaptive Model Tuning
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Solution Overview
Problem
Cuff-less blood pressure measurement systems face limitations in accuracy and require frequent recalibration due to changing conversion coefficients over time, failing to provide reliable measurements across diverse physiological states without the use of invasive cuffs.
Innovation Solution
A cuff-less blood pressure measurement system utilizing a centralized portable scanner with multiple bio-sensors and a signal processor that employs an adaptive BP model, machine learning techniques, and periodic tuning with reference measurements to adjust coefficients, allowing for accurate inference of blood pressure without invasive methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sphygmomanometer (blood pressure cuff) is used to measure blood pressure, then measurement accuracy is improved, but ease of operation deteriorates due to intrusive nature
Solution Approach 1:
The patent extracts the measurement function from the invasive cuff-based sphygmomanometer and implements it through a cuff-less system using optical sensors (photodetectors) and accelerometers to detect physiological signals such as pulse waveforms and motion artifacts, thereby eliminating the need for inflatable cuffs while maintaining measurement capability
Solution Approach 2:
The patent replaces the mechanical inflation/deflation mechanism of the blood pressure cuff with an electronic signal processing system that uses optical detection and machine learning algorithms to infer blood pressure from physiological signals, transforming a mechanical measurement system into an electronic one
2Ease of operation
If a cuff-less blood pressure measurement system is used, then ease of operation is improved, but measurement precision deteriorates due to changing conversion coefficients over time
Solution Approach 1:
The patent implements a dynamic recalibration mechanism where the system periodically performs calibration routines using cuff-based measurements to update conversion coefficients and machine learning model parameters, allowing the system to adapt to changing physiological conditions and maintain accuracy over time
Solution Approach 2:
The patent incorporates feedback loops where measured blood pressure values from cuff-based measurements are used to validate and adjust the cuff-less measurement algorithm, creating a self-correcting system that maintains measurement precision through continuous monitoring and parameter adjustment
3Measurement precision
If frequent recalibration is performed to maintain accuracy, then measurement precision is improved, but loss of time increases due to recalibration procedures
Solution Approach 1:
The patent implements periodic recalibration at scheduled intervals rather than requiring frequent adjustments, allowing the system to maintain acceptable accuracy levels between calibration events while minimizing user burden and time loss
Solution Approach 2:
The patent performs preliminary calibration during initial system setup and uses pre-stored population-based reference data to provide reasonable accuracy before any user-specific calibration is performed, reducing the need for frequent recalibration sessions
Data Source
AI summary
In one embodiment of the invention, a cuff-less blood pressure measuring system is disclosed including cuff-less blood pressure scanner with a vital signs signal processor having an adaptive blood pressure model. A machine learning process is further disclosed to tune the adaptive blood pressure model of the cuff-less blood pressure measuring system to the user.