Non-Invasive Body Component Estimation via Dynamic Spectrum Calibration
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Solution Overview
Problem
Current non-invasive health monitoring technologies face challenges in accurately estimating body components like blood glucose, cholesterol, and other biomarkers outside controlled medical environments, especially in mobile healthcare settings, due to variations in concentration periods and measurement positions.
Innovation Solution
An apparatus and method utilizing a sensor to obtain spectra in different concentration periods, processing these spectra to generate an estimation model based on feature vectors and optimal principal components, and adjusting analysis algorithms and measurement positions for precise estimation of body components, such as blood glucose, cholesterol, and other biomarkers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-invasive spectrum measurement is performed in mobile healthcare settings, then convenience and accessibility are improved, but measurement accuracy deteriorates due to variations in concentration periods and measurement positions
Solution Approach 1:
The system dynamically adapts to varying measurement conditions by automatically selecting optimal analysis algorithms based on the user's physiological state (fasting vs. non-fasting). The measurement position is also dynamically optimized by selecting between different body sites (finger, earlobe, wrist) based on which provides the highest accuracy for the current concentration period, thus maintaining precision despite the mobile, flexible measurement context.
Solution Approach 2:
The system changes measurement parameters (analysis algorithm selection, measurement position selection) based on the concentration period of the body component. By identifying whether the user is in a fasting or non-fasting state, the system adjusts its operational parameters to compensate for variations in spectrum characteristics, thereby maintaining measurement accuracy across different mobile healthcare scenarios.
2Device complexity
If standard analysis algorithms are used for spectrum analysis, then processing simplicity is improved, but estimation accuracy deteriorates due to individual variations in concentration periods
Solution Approach 1:
The system dynamically changes the analysis algorithm parameter based on the detected concentration period. When the user is identified as being in a fasting state, one algorithm is selected; when in a non-fasting state, a different algorithm is selected. This parameter adaptation allows the system to maintain high estimation accuracy for different physiological conditions without requiring the user to manually configure complex settings.
Solution Approach 2:
The system incorporates feedback mechanisms that evaluate the effectiveness of different analysis algorithms under different concentration periods. By monitoring the relationship between the first spectrum (current measurement) and second spectrum (reference measurement), the system determines which algorithm provides optimal results for the current physiological state, thereby achieving high precision while maintaining operational simplicity.
3Productivity
If spectrum measurement is performed without considering concentration periods, then measurement speed is improved, but estimation accuracy deteriorates
Solution Approach 1:
The system performs preliminary classification of the concentration period (fasting vs. non-fasting state) before executing the main estimation process. By quickly determining the user's physiological state through initial spectrum comparison, the system can then select the appropriate analysis algorithm in advance, maintaining measurement speed while ensuring high accuracy through targeted algorithm selection based on the detected concentration period.
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, accurate estimation of body components without prolonged spectrum measurement, improving accuracy and convenience in mobile health monitoring by adapting to individual variations in measurement positions and concentration periods.
Implementation Method 1
a sensor configured to obtain, from an object of a user, a first spectrum in a first concentration period of a body component and a second spectrum in a second concentration period of the body component
Data Source
AI summary
An apparatus and a method for estimating a body component are provided. According to an example embodiment, the apparatus for estimating a body component includes: a sensor configured to obtain, from an object of a user, a first spectrum in a first concentration period of a body component and a second spectrum in a second concentration period of the body component; and a processor configured to extract a first feature vector based on the first spectrum and the second spectrum, to extract a second feature vector based on a standard spectrum and the second spectrum, and to perform first calibration by generating an estimation model for estimating the body component, generation of the estimation model being based on a similarity between the first feature vector and the second feature vector.