Sensor Data Stream Event Identification via Relationship Analysis
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Solution Overview
Problem
Existing techniques are inadequate for effectively identifying event occurrences from sensor data streams, lacking efficiency in analyzing and notifying users of relevant events.
Innovation Solution
A method that accesses multiple sensor data streams, identifies relationships between them, and sends notifications of event occurrences, utilizing metadata and sensor value ranges to determine event correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor data streams are analyzed to identify event occurrences, then event detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex analysis task by dividing sensor data streams into individual channels, each processed separately through standardized pipelines. Each sensor type (accelerometer, gyroscope, microphone, camera) has its own processing module that extracts features independently, then results are integrated to determine event occurrence. This segmentation reduces overall system complexity while maintaining detection accuracy.
Solution Approach 2:
The patent implements a universal event detection framework that handles multiple sensor types through a common architecture. The system uses a standardized data stream processing pipeline that can accommodate different sensor modalities (audio, video, motion sensors) with unified event identification logic. This multi-functional approach improves event detection accuracy across diverse sensor inputs without proportionally increasing system complexity.
2Reliability
If sensor data streams are continuously monitored and analyzed, then event detection reliability is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic sampling of sensor data streams rather than continuous monitoring. Data is collected at predetermined time intervals, and analysis is performed periodically on these sampled data sets. This periodic approach maintains event detection reliability by capturing sufficient data points while significantly reducing energy consumption compared to continuous real-time analysis.
Solution Approach 2:
The patent applies partial monitoring by selectively analyzing only certain sensor data streams or features based on current context or trigger conditions. Instead of processing all sensor data continuously, the system activates full analysis only when preliminary indicators suggest an event may be occurring, thereby maintaining reliability for critical events while reducing overall energy consumption during normal operation.
Data Source
AI summary
According to one embodiment of the present invention, a method for identifying the occurrence of an event from sensor data streams may be provided. The method may include accessing a plurality of sensor data streams generated by a plurality of sensor sets. Each sensor set may comprise one or more sensors. A sensor data stream may be associated with a user. The user may be co-located with a sensor set that generates the sensor data stream. A relationship between two or more sensor data streams of the plurality of sensor data streams may be identified. The method may further include determining, according to the relationship, that the plurality of sensor data streams corresponds to an event. A notification of an occurrence of the event may be sent.