AR Virus Risk Detector Using Logarithmic Merger ML
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Solution Overview
Problem
Current technologies fail to accurately and dynamically assess an individual's risk of disease transmission or contraction in real-time, as they do not consider health status, environmental factors, or up-to-date actions, and are not performed in real-time.
Innovation Solution
A logarithmic merger machine learning model processes inputs from a covering detection model, spatial proximity model, and other models to generate a predicted disease score for a target user, incorporating health history, age, illness observations, and environmental data, providing a real-time risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time processing of multiple machine learning models is implemented, then measurement precision of disease risk assessment is improved, but device complexity increases
Solution Approach 1:
The system is divided into multiple specialized machine learning models, each handling a specific aspect of risk assessment: covering detection model for protective covering status, spatial proximity model for distance calculation, health history model for medical background, age model for demographic risk, illness observation model for current symptoms, and environmental model for contextual factors. This segmentation allows each model to be optimized for its specific function while collectively achieving comprehensive and accurate risk assessment.
Solution Approach 2:
The logarithmic merger model serves as a universal integration component that processes outputs from all six specialized models and generates a unified predicted disease score. This multi-functional approach consolidates multiple assessment dimensions into a single comprehensive metric, maintaining high measurement precision while providing a unified interface that simplifies the overall system architecture.
2Reliability
If multiple machine learning models are processed in real-time, then reliability of risk evaluation is improved, but productivity decreases
Solution Approach 1:
All six machine learning models are executed simultaneously in parallel rather than sequentially, and the system continuously processes incoming video stream data objects as they arrive. This preliminary and concurrent action ensures that the predicted disease score is generated in real-time with each new video input, maintaining high reliability through comprehensive multi-model assessment without sacrificing processing speed through sequential bottlenecks.
3Measurement precision
If comprehensive data from multiple models is integrated, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The logarithmic merger model transforms the outputs from six different models (covering detection, spatial proximity, health history, age, illness observation, and environmental factors) into a unified predicted disease score using a logarithmic calculation. This parameter transformation approach efficiently integrates diverse data dimensions while managing computational complexity through a mathematically optimized merging function that produces an accurate composite risk assessment.
Data Source
AI summary
There is a need to accurately and dynamically evaluate an individual's risk associated with the transmission or contraction of a disease. This need can be addressed, for example, by generation of a real-time or near real-time predicted disease score for an associated user. In one example, a method includes receiving a video stream data object depicting a visual representation of a target user; processing the video stream data object to generate a protective covering indication with respect to the target user; processing the video stream data object to generate a spatial proximity determination score with respect to the target user; processing the protective covering indication and spatial proximity determination score to generate a predicted disease score associated with the target user; and providing an augmented reality video stream data object configured to depict the visual representation of the target user and the predicted disease score.