Edge AI Emergency Detection for Faster Shooter Response
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Solution Overview
Problem
Existing emergency response systems often fail to provide timely and accurate guidance to users and emergency responders due to lack of real-time monitoring and effective communication during unexpected events, leading to potential risks to safety and wellbeing.
Innovation Solution
A system utilizing AI and machine learning techniques deployed on the edge to monitor environments, detect emergencies, classify them in real-time, and generate automatic response information for users and responders using sensor devices integrated into existing infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time monitoring and AI classification systems are deployed to detect and respond to emergencies, then response time and accuracy are improved, but device complexity and implementation cost increase
Solution Approach 1:
The system segments the emergency detection and response process into distinct functional modules: sensor data collection, AI-based event classification, emergency determination logic, and response generation. Each module operates independently but communicates through standardized interfaces, allowing the complex system to be developed, deployed, and maintained in manageable parts while achieving real-time performance
Solution Approach 2:
The system performs preliminary classification of sensor events using AI models before full emergency determination is made. By pre-processing sensor data and identifying potential emergency patterns in advance, the system reduces the computational burden during critical response moments and accelerates overall detection speed without sacrificing accuracy
2Measurement precision
If comprehensive sensor monitoring is implemented across all locations, then emergency detection accuracy is improved, but loss of user privacy and data security risks increase
Solution Approach 1:
The system applies different monitoring strategies and data processing levels to different locations and contexts within the facility. Sensitive areas receive enhanced monitoring only when specifically triggered or during high-risk periods, while other areas use standard monitoring protocols. This localized approach maintains detection accuracy where needed while minimizing privacy intrusion in less critical zones
Solution Approach 2:
The AI classification system acts as an intermediary layer between raw sensor data and emergency determination. It processes and anonymizes sensor information, extracting only essential emergency-relevant features while discarding personally identifiable details. This intermediary processing layer enables accurate emergency detection while preserving user privacy by preventing direct collection or storage of sensitive personal data
Data Source
AI summary
Disclosed are system and techniques for classifying events such as emergencies. A system can include a computer system to perform operations including: receiving sensor signals from a group of devices at a location, determining whether one or more of the sensor signals exceed expected threshold levels, in response to determining that the one or more of the sensor signals exceed the expected threshold levels, correlating the sensor signals, classifying the correlated sensor signals into an emergency event based on applying an artificial intelligence (AI) model to the correlated sensor signals, the AI model having been trained to classify the correlated sensor signals into a type of emergency, determine a spread of the emergency event, and determine a severity level of the emergency event, generating, based on information associated with the classified emergency event as output from the AI model, emergency response information, and returning the emergency response information.