Mobile Emergency Detection via Predictive Sensor Analysis
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Solution Overview
Problem
Existing emergency detection systems are ineffective in timely reporting of personal emergencies, especially when individuals are alone or in secluded locations, as they rely on others to witness and report incidents, leading to delayed assistance.
Innovation Solution
A mobile application and backend system that continuously monitor sensor data and contextual information to predict and detect emergency events, alerting users and emergency contacts, and automatically notifying services through a customizable emergency predictive model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing emergency detection systems rely on witnesses to report incidents, then device complexity is reduced, but detection reliability and response time deteriorate when individuals are alone or in secluded locations
Solution Approach 1:
The system enables self-service by allowing the monitored individual to directly initiate emergency reporting through the mobile application without requiring external witnesses. The application detects emergency situations through sensor data analysis and automatically triggers alerts to emergency contacts and services, making the reporting process independent of bystanders.
Solution Approach 2:
The system performs preliminary action by continuously monitoring sensor data and pre-configuring emergency contact information before emergencies occur. The mobile application maintains ready-to-use alert mechanisms and emergency contact lists, enabling immediate response when emergencies are detected without requiring setup or witness intervention at the moment of incident.
2Measurement precision
If continuous monitoring of sensor data is implemented, then emergency detection precision is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic action by monitoring sensor data at optimized intervals rather than continuously. The mobile application analyzes sensor readings at regular periods and only triggers emergency alerts when abnormal patterns are detected, reducing energy consumption while maintaining detection precision through strategic sampling of health and environmental data.
Solution Approach 2:
The system applies parameter changes by adjusting monitoring thresholds and sensitivity levels based on user activity states. The application modifies detection parameters dynamically - for example, increasing sensitivity during high-risk activities or time periods while reducing monitoring intensity during low-risk states, thereby balancing detection precision with energy conservation.
Data Source
AI summary
According to at least one aspect, a system and corresponding method for detecting and reporting personal emergency events includes a mobile application running on a client device of a user and a backend application running on a backend system. The mobile application and the backend application can communicate on a regular basis. The mobile application or the backend application can regularly monitor sensor data, user contextual data or other data indicative of user state of health, activities or environment and detect or predict an emergency event of the user based on the monitored data. Upon detecting an emergency event, the mobile application or the backend application can warn the user or notify emergency contacts of the user of the emergency event. The backend application can use the sensor data or other user data to update an emergency predictive model specific to the user employed to detect or predict emergency events.