Smartphone Deep Learning Personal Danger Detection
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Solution Overview
Problem
Current systems lack an effective method to proactively detect personal danger and respond appropriately, especially in situations where individuals are already in physical danger, as existing devices require manual initiation of emergency calls and do not assess risk levels.
Innovation Solution
A deep learning system on a mobile device monitors user safety by comparing current environmental information to a routine profile, generating a risk score, and sending alerts or emergency messages based on predefined thresholds, using location, audio, and other metrics to assess potential danger.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual emergency call systems are used, then device complexity is reduced, but response time and effectiveness in personal danger detection deteriorate
Solution Approach 1:
The system performs preliminary actions by continuously monitoring environmental data and comparing it against stored routine profiles before danger occurs. The deep learning system pre-processes location, audio, and sensor data to establish baseline safety patterns, enabling proactive danger detection rather than reactive response
Solution Approach 2:
The system serves itself by automatically analyzing environmental information against user routine profiles without requiring manual intervention. The deep learning model autonomously generates risk scores and triggers alerts when danger thresholds are exceeded, eliminating the need for users to manually assess or report danger
2Measurement precision
If deep learning analysis is implemented, then personal danger detection accuracy is improved, but device complexity and processing requirements worsen
Solution Approach 1:
The system segments the danger detection process into distinct functional modules: data collection from multiple sensors, environmental information processing, routine profile comparison, risk score generation, and alert triggering. This modular segmentation allows the deep learning system to handle complex analysis through specialized sub-components rather than a monolithic system
Solution Approach 2:
The system adds temporal dimensionality by comparing current environmental data against historical routine profiles stored in the database. The deep learning model analyzes patterns across time dimensions, comparing present conditions with past safe conditions to generate comprehensive risk assessments that consider both spatial and temporal factors
3Reliability
If continuous monitoring is performed, then personal safety detection capability is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic monitoring by continuously collecting environmental data and comparing it against routine profiles at regular intervals. The deep learning model processes data in periodic cycles, generating risk scores at scheduled times rather than requiring constant real-time analysis, thus reducing overall energy consumption while maintaining effective safety monitoring
Solution Approach 2:
The system applies partial monitoring by focusing computational resources on analyzing only the most critical environmental parameters and comparing them against key aspects of routine profiles. The deep learning model performs selective analysis rather than exhaustive processing of all available data, reducing energy consumption while maintaining adequate detection capability
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.