Personalized Stress Estimation via PPG and Artery Models
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Solution Overview
Problem
Current stress estimation methods using Photoplethysmograph (PPG) signals are not personalized and do not account for individual physiological variations, lacking quantitative measures to compare stress levels between individuals or within the same person, and fail to accurately reflect stress responses influenced by systems like the rennin-angiotensin system and fluid structure interactions.
Innovation Solution
A system comprising a PPG sensor, preprocessor, and processor that generates a processed PPG signal by creating numerical solutions to mathematical models of blood pressure regulation and radial artery dynamics, training an inference engine using an artificial neural network to produce a stress parameter, which accounts for kidney and baroreceptor variations and fluid structure interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PPG-based stress estimation methods are used, then stress measurement can be obtained, but the measurement is not personalized and does not account for individual physiological variations
Solution Approach 1:
The patent applies local quality by creating personalized mathematical models for each individual based on their specific physiological characteristics. The system estimates personal parameters such as pulse wave velocity, arterial stiffness, and blood pressure specific to each person, rather than using universal thresholds. This allows the stress estimation to be tailored to individual physiological variations, improving both accuracy and personalization simultaneously.
2Measurement precision
If simple PPG signal analysis is used, then the system is easy to operate, but it cannot provide quantitative stress estimates for comparison between individuals
Solution Approach 1:
The patent introduces mathematical models as an intermediary between the simple PPG signal and the complex physiological parameters. These models act as mediators that translate the easily acquired PPG signal into quantitative stress estimates by incorporating physiological relationships. This allows the system to maintain operational simplicity while achieving accurate quantitative stress measurement through the intermediary modeling layer.
Solution Approach 2:
The system changes parameters by estimating individual-specific physiological parameters (pulse wave velocity, arterial stiffness, blood pressure) from the PPG signal rather than using fixed thresholds. By dynamically adjusting these parameters based on each person's characteristics, the system achieves quantitative stress estimation capability without requiring complex hardware, resolving the contradiction between measurement precision and device complexity.
3Measurement precision
If conventional stress measurement methods are used, then stress detection is possible, but they fail to account for physiological systems like rennin-angiotensin system and fluid structure interactions
Solution Approach 1:
The patent applies preliminary action by pre-establishing mathematical models that incorporate complex physiological systems (rennin-angiotensin system, fluid structure interactions, baroreceptor responses) before actual stress measurement. These models are prepared in advance with known physiological relationships, allowing the system to account for these complex factors during stress estimation without adding real-time computational complexity. The physiological accuracy is achieved through these pre-configured models.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time, personalized, and quantitative stress estimation, allowing for comparison between individuals and within the same person, effectively capturing stress patterns influenced by autonomic nervous system and hormonal responses.
Implementation Method 1
PPG is the most commonly used technology for stress estimation. PPG indicates a signal corresponding to the quantity of light reflected from a selected part of a human body after being irradiated by light having a particular wavelength emitted from a light source of a light emitting device.
Implementation Method 2
PPG indicates a signal corresponding to the quantity of light reflected from a selected part of a human body after being irradiated by light having a particular wavelength emitted from a light source of a light emitting device.
Data Source
AI summary
A system and method for determining a stress level of a person is provided. The system creates a numerical solution to mathematical model of a blood pressure (BP) regulation, lumped mathematical model of a radial artery and partial differential equation (PDE) model of the radial artery. These models are then used to generate an inference engine using the mathematical model of blood pressure (BP) regulation, the lumped mathematical model of the radial artery and the PDE model of the radial artery. The inference engine is trained using an artificial neural network technique. At the same time the PPG signal of the person is sensed and preprocessed. The preprocessed PPG signal is then given to the trained inference engine. The trained inference engine generates a stress parameter corresponding to the person based on the processed PPG signal.