Fall Detection Server Correlating Multi-Source Sensor Data
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Solution Overview
Problem
Existing systems for detecting fall events in busy facilities often incorrectly identify or miss fall events due to the lack of direct correlation between sensor data and actual user experiences.
Innovation Solution
A server system that collects and correlates sensor data from various devices and sensors within a facility, identifies a subset of relevant sensors based on event indicators, and analyzes the data to detect and confirm candidate fall events, sending notifications to associated devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If device-integrated and fixed sensors are used to detect fall events, then fall detection coverage is improved, but measurement precision deteriorates due to inability to directly correlate sensor data with actual user fall events
Solution Approach 1:
The patent combines data from multiple sensors (device-integrated sensors, fixed sensors, and mobile devices) to create a comprehensive view of user activity. By merging sensor data with user profile information and activity context, the system achieves both wide coverage and high precision in fall event detection.
Solution Approach 2:
The system introduces an intermediary processing layer that correlates sensor data with user context and activity information. This intermediary layer acts as a mediator between raw sensor signals and fall event determination, enabling accurate correlation between sensor data and actual user fall events while maintaining broad detection coverage.
2Measurement precision
If multiple sensors are correlated to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the fall detection process into distinct modules: data collection from multiple sensors, user profile management, activity recognition, and fall event determination. This segmentation reduces overall system complexity by making each component independent and manageable while maintaining high detection precision through their coordinated operation.
Solution Approach 2:
The system employs multi-functional processing units that can handle various sensor types and data formats uniformly. The server infrastructure provides universal capabilities to correlate data from diverse sources (fixed sensors, mobile devices, wearables) using the same processing algorithms, thereby improving detection accuracy without proportionally increasing system complexity.
Data Source
AI summary
An example server includes a memory configured to store sensor data from a plurality of sensors in a facility; a communications interface; and a processor interconnected with the memory and the communications interface, the processor configured to: in response to receiving, via the communications interface, an event indicator from a source sensor of a client device: identify a subset of the plurality of sensors based on the event indicator; retrieve and correlate the sensor data from the identified subset of the plurality of sensors in the facility; detect a candidate event associated with a user of the client device from the correlated sensor data; and when the candidate event is detected, send an event notification to the client device.