Mobile Device Emergency Detection via Sensor Classification
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Solution Overview
Problem
There is a need for effective behavioral monitoring and emergency situation detection for vulnerable populations such as children, the elderly, and those with disabilities or recent trauma, especially when they are not under supervised care, as timely detection of emergencies like disorientation, seizures, or physical injuries is critical to prevent harm or fatalities.
Innovation Solution
A computer-implemented method using sensor data from mobile devices to predict user states, transmitting confirmation requests to users, and sending notifications to secondary users about predicted emergency situations, employing a system with a notification manager, classifier engine, and alert interface to determine and respond to emergency conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If active behavioral monitoring is implemented for vulnerable populations, then emergency detection capability is improved, but device complexity and energy consumption increase
Solution Approach 1:
The system divides monitoring functions into separate modules: sensor data collection, classification algorithm execution, and notification delivery. Each module operates independently and can be optimized separately, reducing overall system complexity while maintaining comprehensive monitoring capability.
Solution Approach 2:
A classification engine acts as an intermediary between raw sensor data and emergency notifications. This intermediate layer processes and interprets sensor inputs using machine learning models, filtering out false alarms while maintaining detection accuracy without requiring direct complex analysis at the notification stage.
2Loss of time
If continuous sensor monitoring is performed, then emergency detection timeliness is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic sensor sampling at optimized intervals rather than continuous monitoring. The classification engine processes sensor data at strategic time points when emergency conditions are most likely to occur, maintaining detection timeliness while significantly reducing energy consumption compared to continuous monitoring.
Solution Approach 2:
The system dynamically adjusts monitoring parameters such as sensor sampling frequency and classification threshold sensitivity based on contextual factors like time of day, location, and user behavior patterns. This adaptive approach maintains emergency detection capability during critical periods while reducing energy consumption during lower-risk periods.
3Measurement precision
If machine learning classification algorithms are used, then emergency situation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The classification engine is pre-trained on extensive datasets of normal and emergency behaviors, storing learned patterns and decision boundaries. During actual operation, the system retrieves and applies pre-learned classification rules rather than performing complex real-time training, achieving high accuracy with minimal processing delay.
Solution Approach 2:
The system applies classification algorithms selectively only when sensor data indicates potential emergency conditions, rather than continuously processing all inputs through the full ML pipeline. This partial application approach maintains high accuracy for actual emergencies while reducing overall processing time and computational resource consumption.
Data Source
AI summary
A computer-implemented method is provided including receiving sensor data from a mobile device corresponding to a first user. A user state of the first user is predicted based on the sensor data. A request is transmitted to the first user to confirm the predicted user state, and a notification is transmitted regarding the predicted user state to a second user responsive to the first user's confirmation of the predicted user state or the first user's failure to respond to the request. A computing system for monitoring and reporting activity of a mobile device is also provided.