Infection Risk Assessment Using IoT Data and Attribute Weights
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Solution Overview
Problem
Current health risk assessment technologies are inadequate due to reliance on outdated and self-reported data, lacking accuracy and timeliness, especially in assessing individual and location-specific infection risks during pandemics, and are not accessible or affordable for all.
Innovation Solution
A computer-implemented method and system that processes individual and location data using specialized algorithms to determine infection risk levels by applying attribute weights to demographic, medical, and location-based factors, incorporating IoT devices for real-time data collection and alert generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-reported data and outdated data are used for health risk assessment, then the system is simple and accessible, but the accuracy and timeliness of infection risk assessment deteriorates
Solution Approach 1:
The patent combines multiple data sources (self-reported data, IoT device data, location data, mobile device data) into a unified risk assessment system. This merging allows the system to maintain simplicity for users while incorporating diverse data streams to improve accuracy and timeliness of infection risk assessment.
Solution Approach 2:
The system is designed to process multiple types of data (demographic, medical, location, sensor data) through a single platform that can assess both individual and location-specific infection risks. This multi-functionality allows the system to handle various data sources uniformly, improving accuracy without proportionally increasing complexity.
2Loss of time
If comprehensive individual and location data are processed to determine infection risk levels, then the timeliness and accuracy of risk assessment improves, but the computational resources and system complexity increases
Solution Approach 1:
The system performs preliminary processing of data by collecting and organizing information from multiple sources (IoT devices, mobile devices, location services) before risk calculation. This preliminary action prepares data in advance, enabling faster real-time risk assessment without overwhelming computational resources during critical evaluation moments.
Solution Approach 2:
The risk assessment system is divided into modular components: data collection modules, data processing modules, risk calculation modules, and alert generation modules. This segmentation allows each component to handle specific tasks efficiently, reducing overall system complexity while maintaining timeliness through parallel processing capabilities.
3Loss of time
If real-time data collection using IoT devices is implemented, then the timeliness of infection risk detection improves, but the cost and device complexity increases
Solution Approach 1:
The system utilizes existing mobile devices and IoT devices that users already possess or have access to, rather than requiring specialized proprietary equipment. These devices self-report data through applications and sensors, eliminating the need for expensive dedicated hardware while maintaining real-time data collection capabilities for timely infection risk detection.
Data Source
AI summary
A computer-implemented method for determining aggregate health risk factors comprising one or more processors configured for receiving individual profile data comprising individual attributes corresponding to an individual, the individual attributes comprising demographic data, medical data, and individual location data. Further, the computer-implemented method may be configured for determining an individual risk factor based at least on the individual profile data. Responsive to the individual risk factor satisfying a first condition, the computer-implemented method may be configured for generating a first alert indicating the individual risk factor for the individual.