Fuzzy Membership Function for Stress Level Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining stress levels using physiological parameters require users to be placed in stressful situations during calibration, which is restrictive and may be compromised by individual variability, and lack sensitivity and specificity in detection.
Innovation Solution
A method using fuzzy logic to determine stress levels based on membership functions, which are calibrated without requiring users to be in a stressed state, by measuring physiological parameters and applying distribution, normalization, and optional standardization functions to optimize the estimation of stress levels, allowing for periodic recalibration and improved sensitivity and specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users are placed in stressful situations during calibration, then the membership function can be determined, but the method becomes restrictive and reliability is compromised due to individual variability
Solution Approach 1:
The patent inverts the traditional calibration approach by determining the membership function during rest state instead of stressed state. The method measures physiological parameters during rest, establishes the membership function boundaries based on rest state distributions, and then uses this pre-established function to evaluate stressed states without requiring users to undergo stressful calibration procedures.
Solution Approach 2:
The system performs self-calibration by automatically measuring physiological parameters during rest state, determining the membership function parameters (such as rest state mean and standard deviation) without requiring external intervention or user participation in stressful situations. The calibration process becomes autonomous and eliminates the need for controlled stressful scenarios.
2Reliability
If traditional calibration methods are used, then stress levels can be determined, but sensitivity and specificity of detection are reduced
Solution Approach 1:
The patent changes the reference parameter from stressed state characteristics to rest state characteristics for defining the membership function. By using rest state physiological parameter distributions (mean, standard deviation) as the baseline, the method creates a more stable and individualized reference that improves both reliability and precision in detecting stress levels without being affected by individual variability in stress responses.
Data Source
AI summary
A method for determining a membership function, the membership functions allowing a stress level of a user to be determined on the basis of a physiological parameter measured on the user, the membership function varying, depending on the physiological parameter, between:a first value, representative of a rest state;and a second value, representative of a stressed state;the membership function taking into account a distribution function, which is defined beforehand, the distribution function being a continuous and monotonic function, the distribution function being applied to a normalized parameter established on the basis of a measured physiological parameter, the method comprising determining a normalization function, and optionally a standardization function, the normalization function and the optional standardization function being determined on the basis of physiological parameters measured on a plurality of test individuals, in various calibration periods.