Body Composition Analysis with Neural Network Chromophore Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for measuring chromophore concentration in body composition are either costly or lack accuracy, making commercialization difficult.
Innovation Solution
A method using a first device for low-cost continuous wave measurements and a second device for high-accuracy frequency domain spectroscopy, combined with a neural network model trained on measurement data to predict chromophore concentrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency domain or time domain scheme is used to measure chromophore concentration, then measurement accuracy is improved, but implementation cost increases
Solution Approach 1:
A neural network model serves as an intermediary that translates simple continuous wave measurement data into accurate chromophore concentration predictions. The model learns the complex relationship between basic optical measurements and actual tissue composition, enabling accurate analysis without requiring complex frequency domain or time domain measurement systems.
Solution Approach 2:
The patent replaces complex mechanical measurement systems (frequency domain modulators, time domain pulse generators) with a computational approach using neural networks. Instead of using complex hardware to directly obtain accurate measurements, the system uses simple optical measurements combined with AI-based processing to achieve the same accuracy.
2Device complexity
If continuous wave scheme is used to measure chromophore concentration, then implementation cost is reduced, but measurement accuracy deteriorates
Solution Approach 1:
The patent replaces the need for complex mechanical measurement systems with a computational neural network model. The continuous wave measurements, which are simple and low-cost, are processed through the neural network to achieve accurate chromophore concentration predictions, effectively substituting mechanical complexity with computational intelligence.
Solution Approach 2:
The neural network model acts as an intermediary that bridges the gap between simple continuous wave measurements and accurate chromophore concentration values. It processes the basic optical data and transforms it into meaningful biological information without requiring complex measurement hardware.
3Measurement precision
If broadband CW scheme combined with frequency domain scheme is used, then chromophore concentration accuracy is improved, but implementation cost increases and commercialization becomes difficult
Solution Approach 1:
The patent extracts the essential information needed for accurate chromophore concentration measurement from complex measurement systems and encapsulates it in a neural network model. By taking out the core computational logic and separating it from the measurement hardware, the system achieves accuracy without requiring expensive combined broadband CW and frequency domain equipment.
Solution Approach 2:
The neural network model creates a virtual copy of the complex measurement and analysis process. Instead of using actual complex measurement systems, the model learns from training data and replicates the analytical capability, providing accurate chromophore concentration results through software rather than hardware complexity.
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
Provides a cost-effective and accurate method for analyzing body composition by predicting chromophore concentrations, enabling simpler and more affordable commercial applications.
Implementation Method 1
training a neural network model by using training data generated based on the first measurement value and the chromophore concentration value; and when the training of the neural network model is completed, acquiring a prediction value of a chromophore concentration by inputting a second measurement value measured in a specific tissue by using the first device into the neural network model
Implementation Method 2
acquiring a first measurement value measured in tissue to be measured by using a first device; the continuous wave (CW) scheme is simple in implementation and low in cost as compared with other schemes
Implementation Method 3
acquiring a chromophore concentration value measured in the tissue to be measured by using a second device different from the first device; the frequency domain or time domain scheme is high in implementation cost as compared with other schemes, while accurate information may be obtained
Data Source
AI summary
Disclosed is a method for analyzing a body component, which is performed by a computing device including at least one processor according to some exemplary embodiments of the present disclosure. The method may include: acquiring a first measurement value measured in tissue to be measured by using a first device; acquiring a chromophore concentration value measured in the tissue to be measured by using a second device different from the first device; training a neural network model by using training data generated based on the first measurement value and the chromophore concentration value; and when the training of the neural network model is completed, acquiring a prediction value of a chromophore concentration by inputting a second measurement value measured in a specific tissue by using the first device into the neural network model.