Mobile Device Personal Danger Detection via Risk Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack an effective method to proactively detect personal danger and respond to situations where individuals perceive or are in physical danger, especially when they are unable to initiate help due to being attacked.
Innovation Solution
A deep learning system integrated into mobile electronic devices that captures a user's habits, sounds, and schedules to assess real-time risk, alerting authorities if a situation exceeds a certain danger threshold, with customizable emergency contact responses based on risk levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a deep learning system is used to proactively detect personal danger, then personal safety monitoring capability is improved, but device complexity and power consumption increase
Solution Approach 1:
The system segments the danger detection function into multiple independent modules: audio analysis module, location analysis module, routine profile module, and risk scoring module. Each module processes specific data types and contributes to the overall risk assessment, allowing the complex deep learning system to be divided into manageable, specialized components that can be optimized independently.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and analyzing user routine data (locations, times, activities) to establish a baseline profile before danger occurs. This pre-established routine profile enables the system to quickly compare current conditions against normal behavior patterns, allowing for rapid danger detection without requiring complex real-time analysis of all possible variables.
2Measurement precision
If real-time environmental information is continuously monitored, then danger detection accuracy is improved, but power consumption and data transmission load increase
Solution Approach 1:
The system implements feedback mechanisms where the deep learning model analyzes incoming environmental data (audio, location) and provides risk score feedback. When the risk score exceeds a threshold, the system triggers alerts and notifications. This feedback loop allows the system to adjust monitoring intensity based on current risk levels, maintaining high detection accuracy while reducing power consumption during low-risk periods.
Solution Approach 2:
The system changes monitoring parameters dynamically based on user behavior patterns and environmental context. Instead of continuous high-intensity monitoring, the system adjusts sampling rates, activation thresholds, and data collection frequency based on the user's routine profile and current risk assessment, optimizing the balance between detection accuracy and power consumption.
3Productivity
If multiple emergency contacts are designated with different risk levels, then emergency response effectiveness is improved, but system configuration complexity increases
Solution Approach 1:
The system applies local quality by assigning different risk level thresholds and contact priorities to different emergency contacts based on their relationship to the user and appropriate response scenarios. Each contact is configured with specific activation conditions (e.g., low-risk contacts for minor issues, high-risk contacts for severe dangers), allowing the system to automatically select the appropriate contact based on the current risk assessment without requiring complex user intervention.
Data Source
AI summary
A mobile electronic device such as a smartphone is used in conjunction with a deep learning system to detect and respond to personal danger. The deep learning system monitors current information (such as location, audio, biometrics, etc.) from the smartphone and generates a risk score by comparing the information to a routine profile for the user. If the risk score exceeds a predetermined threshold, an alert is sent to the smartphone which presents an alert screen to the user. The alert screen allows the user to cancel the alert (and notify the deep learning system) or confirm the alert (and immediately transmit an emergency message). Multiple emergency contacts can be designated, e.g., one for a low-level risk, another for an intermediate-level risk, and another for a high-level risk, and the emergency message can be sent to a selected contact depending upon the severity of the risk score.