Ambient Emergency Detection via Sensor ML and Verification
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Solution Overview
Problem
Existing safety solutions for mobile devices do not adequately address the need for timely and automatic detection and response to emergency situations, particularly in scenarios involving direct interaction with unfamiliar people and places, leading to hesitancy in using e-commerce services that require such interactions.
Innovation Solution
A portable safety monitoring system that uses machine learning models to detect potential emergencies through sensor signals from mobile devices, verifies the situation via a verification request, and automatically contacts emergency services if the situation is confirmed, ensuring user safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual emergency contact methods are used, then user control is maintained, but response time is delayed due to user incapacitation or inability to access the device
Solution Approach 1:
The system enables self-service by automatically detecting emergencies through sensor data and initiating emergency contacts without user intervention. The mobile device autonomously monitors vital signs, detects abnormal patterns indicating emergencies, and automatically contacts emergency services, eliminating the need for user action during incapacitating situations.
Solution Approach 2:
The system performs preliminary actions by pre-configuring emergency contact information and establishing automated monitoring protocols before emergencies occur. Emergency contacts are pre-programmed, and continuous sensor monitoring is established in advance, enabling immediate automated response when emergencies are detected without requiring user setup or decision-making during critical moments.
2Loss of time
If automated emergency detection systems are implemented, then response time is reduced, but false alarms may increase
Solution Approach 1:
The system implements feedback by continuously monitoring sensor data and comparing current readings against established baseline patterns. When abnormal patterns are detected, the system provides feedback through automated verification processes, contacting emergency contacts to confirm whether the detected anomaly represents a true emergency or a false alarm, thereby improving detection accuracy while maintaining rapid response capability.
Data Source
AI summary
An embodiment includes receiving, by a processor, a sensor signal from a monitoring sensor during a scheduled monitoring session for monitoring a first user. An embodiment includes processing, by the processor, the sensor signal using a machine learning (ML) model such that the ML model outputs an indication of whether the first user is experiencing a potential emergency. An embodiment includes performing, by the processor in response to the ML model indicating that the first user is experiencing a potential emergency, a verification routine that includes transmitting a verification request and, upon detecting a lack of response to the verification request within a predetermined amount of time, confirming the potential emergency as an actual emergency. An embodiment includes requesting, by the processor automatically in response to the verification routine confirming that the potential emergency is an actual emergency, dispatch of emergency services to a location of the first user.