Portable Escape Guidance Device Using Machine Learning Venue Models
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Solution Overview
Problem
Current navigation and direction devices are insufficient in emergency situations, as they do not provide specific information about the exact location, nature of the emergency, and available escape routes, which can lead to increased risks for individuals.
Innovation Solution
A portable escape guiding device that uses machine learning to construct a venue model based on pre-entry information and sensor data, generating an escape plan when a threat is detected, including warnings and guidance for safe egress, tailored to the user's location and abilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current navigation devices are used in emergency situations, then general direction guidance is provided, but specific emergency escape information is insufficient
Solution Approach 1:
The system segments information collection into multiple specialized sensors (motion sensors, environmental sensors, communication modules) that gather specific data types separately, then integrates them into a comprehensive escape plan. This segmentation allows each sensor to focus on specific emergency-relevant parameters while maintaining overall system reliability.
Solution Approach 2:
The system performs preliminary actions by continuously collecting venue layout data, sensor information, and user profile data before emergencies occur. The processor pre-processes this information and stores it ready for rapid escape plan generation when threats are detected, eliminating delays during critical moments.
2Reliability
If a comprehensive sensor system is deployed to detect all hazards, then hazard detection capability is improved, but device complexity increases
Solution Approach 1:
The system employs multi-functional sensors that can detect multiple types of hazards simultaneously. For example, environmental sensors can detect smoke, temperature changes, and gas composition, while communication modules can receive both emergency alerts and venue information. This universality reduces the total number of specialized sensors needed while maintaining comprehensive hazard detection capability.
Solution Approach 2:
The processor acts as an intermediary that integrates data from multiple sensors and external sources. Rather than requiring complex direct connections between all sensors and output devices, the processor mediates information flow, consolidating sensor inputs and generating coordinated escape guidance, thereby simplifying system architecture.
3Speed
If escape plans are generated in real-time during emergencies, then response speed is improved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary data collection and processing before emergencies occur. Venue layouts, sensor calibrations, user profiles, and escape route databases are pre-loaded and organized during normal operations. When emergencies occur, the processor only needs to integrate current sensor readings with pre-processed data, dramatically reducing real-time computational requirements and accelerating escape plan generation.
Solution Approach 2:
The escape plan generation process is dynamic and adaptive. The system continuously updates escape routes based on real-time sensor feedback and changing conditions. If new hazards are detected or routes become blocked, the processor dynamically recalculates alternative paths using pre-established algorithms, maintaining response speed while adapting to evolving emergency situations.
Data Source
AI summary
A device and associated methods for escaping from a venue when a threat is detected is described. Venues can be buildings or outside areas and contain the area where the threat constitutes a hazard to a protected person. Threats include fire, terrorists, gunmen, explosion, collapse, loss of critical resources and crowd panic. The device incorporates a machine learning system implemented with a neural network or other pattern matching system and is trained in steps. Pre-training is based on general requirements such as edge-detection and audio analysis. Principles and data for venue layouts and human behavior can be included. The produced model is further trained from data gathered from sensors and servers after entry into the venue. Operation of the model produces warnings of threats and a plan of escape with steps of the plan communicated to the protected person by audio, visual or tactile sensory channels.