Automatic User Risk Detection Using Multi-Source Sensor Fusion
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Solution Overview
Problem
Existing personal security solutions require manual activation by users, which is impractical in dangerous situations where the user is unconscious or unable to access their phone, such as during mugging, assault, or health emergencies.
Innovation Solution
An apparatus and method that determine user risk using multiple data types, including movement, biometric, audio, and image data, to automatically initiate an alarm and alert emergency contacts when a predetermined threshold is exceeded, even if the user is unable to manually trigger the alert.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual alert solutions are used, then users can control when alerts are sent, but users cannot trigger alerts when unconscious or unable to access their phone
Solution Approach 1:
The system automatically monitors multiple data sources (movement sensors, biometric sensors, audio, image) and autonomously determines when to trigger alerts without requiring user intervention. The processor continuously analyzes data patterns and initiates alarm sequences independently, allowing the system to protect users even when they are unconscious or incapacitated.
Solution Approach 2:
The system performs preliminary monitoring and analysis of user status using multiple sensors before a dangerous situation fully develops. By continuously tracking movement patterns, biometric data, and environmental factors, the system can detect early signs of distress and prepare to trigger alerts automatically before the user loses the ability to manually activate them.
2Measurement precision
If multiple data types are collected and analyzed, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system divides the complex monitoring task into separate functional modules: movement data collection and analysis, biometric data collection and analysis, audio data collection and analysis, and image data collection and analysis. Each module processes its specific data type independently, and the processor integrates the results to determine overall risk level. This modular approach improves detection accuracy while managing system complexity through organized functional separation.
Data Source
AI summary
For determining user risk using multiple data types, an apparatus is disclosed. A system, method, and program product also perform the functions of the apparatus. The apparatus for determining user risk using multiple data types includes a processor and a memory. The memory stores code executable by the processor. The processor receives first data about a user and determines a first probability of the user being at risk using the first data. In response to the first probability exceeding a first threshold, the processor receives second data, the second data being a different type of data than the first data. The processor also determines a second probability of the user being in danger using the second data. In response to the second probability exceeding a second threshold, the processor initiates an alarm.