Mobile Urinalysis via Image Color Correction
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Solution Overview
Problem
Current urinalysis methods require skilled personnel and are susceptible to errors, lacking a cost-effective solution for conducting analyses in various environments without specialized equipment.
Innovation Solution
A method utilizing a mobile camera to analyze urine test strips by capturing images, processing them with a mobile device that includes a memory unit with reference charts and color scales, converting color changes into actionable data for user recommendations, and minimizing background interference through frame areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If skilled personnel manually analyze urine test strips by observation, then the analysis can be performed with simple equipment, but the process is susceptible to human error and requires specialized training
Solution Approach 1:
The patent replaces the manual mechanical observation system with an automated image processing system. A mobile camera captures images of the test strip, and computer vision algorithms automatically analyze color changes and patterns, eliminating human error while maintaining simplicity of operation.
Solution Approach 2:
The system enables self-service urinalysis by allowing any user to perform the test independently using a mobile device. The automated image analysis eliminates the need for skilled personnel, making the system accessible to untrained individuals while maintaining reliable results through algorithmic analysis.
2Measurement precision
If automated image processing systems are used to analyze urine test strips, then measurement precision and reliability improve, but device complexity and cost increase
Solution Approach 1:
The patent makes the system universal by using a mobile camera instead of specialized equipment. The same mobile device that captures the image also runs the image processing algorithms, eliminating the need for separate complex analysis hardware and reducing overall system complexity.
Solution Approach 2:
The system creates a digital copy (image) of the test strip and analyzes this copy using software algorithms rather than requiring complex physical measurement devices. This copying approach simplifies the hardware while maintaining precise measurement capabilities through computational analysis.
3Measurement precision
If reference color charts and calibration areas are included in the test strip, then color analysis accuracy improves, but the test strip structure and manufacturing complexity increase
Solution Approach 1:
The patent merges the reference color charts, calibration areas, and test strip into a single integrated component. These elements are combined in one image capture, eliminating the need for separate calibration devices and simplifying the overall system while maintaining high color analysis accuracy.
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 convenient urinalysis in any environment, reducing human error and costs, allowing unskilled individuals to obtain accurate results, and facilitating retrospective monitoring of user profiles.
Implementation Method 1
capturing an image of a urine strip... taking an image of the urine test strip
Implementation Method 2
determining the color changes that has occurred in the reaction area... obtaining a urinalysis result by matching the data on the color changes
Data Source
AI summary
A method for conducting a urinalysis is provided. The method includes receiving an image of a urine strip having a plurality of reacting areas configured to react with a predetermined urine parameter, and a plurality of reference regions each having a designated color; extracting, from each reference region, reference values representative of a detected color in the reference region; extracting, from each reacting area, color values representative of a detected color of the reacting area; conducting a regression analysis by determining least-squares of the reference values in accordance with prestored set of values corresponding to expected colors of each reference region; determining a color correction model by calculating root polynomial expansion of the least-squares; applying the color correction model on the color values by calculating root polynomial expansion of the color values to obtain normalized values; and determine level of the urine parameters in accordance with normalized values.


