Signal Processing System for Multi-Sensor Correlation
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Solution Overview
Problem
Existing signal processing methods for detecting physiological or environmental changes are limited by their inability to effectively combine and correlate multiple independent signals from different scales and systems, leading to higher false positive rates and a lack of real-time monitoring capabilities.
Innovation Solution
A method that converts temporal signals into asynchronous event signals, analyzes activity profiles, and determines meta-contexts to identify correlations among signals, allowing for more precise monitoring and prediction of individual or environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple independent signals from different sensors are processed separately using isolated feature detection, then the processing simplicity is maintained, but the detection accuracy and reliability deteriorate due to inability to correlate signals and higher false positive rates
Solution Approach 1:
The patent segments the signal processing into distinct modules: a feature detection module that identifies predefined features in each sensor signal, and a correlation module that processes these features across multiple signals. This segmentation allows complex multi-signal analysis to be broken down into manageable steps while maintaining the ability to correlate information across different sensor types.
Solution Approach 2:
The patent introduces an intermediary representation layer using binary vectors to encode the presence or absence of detected features. This intermediary format serves as a bridge between the raw sensor signals and the final correlation analysis, enabling systematic comparison and correlation of features across multiple independent signals without direct complex processing of the original signals.
2Measurement precision
If isolated signals are processed independently, then the computational requirements are reduced, but the measurement precision deteriorates due to lack of contextual correlation between signals
Solution Approach 1:
The patent performs preliminary feature detection and encoding of sensor signals into standardized binary vectors before correlation analysis. This preliminary processing extracts and prepares the essential information in advance, reducing the computational burden during the correlation phase while ensuring precise detection of signal changes through systematic comparison of pre-processed features.
3Speed
If real-time monitoring of multiple signals is implemented, then the responsiveness to changes is improved, but the system complexity increases due to the need to correlate signals from different scales and systems
Solution Approach 1:
The patent implements periodic correlation analysis where the system continuously monitors sensor signals, detects features, and performs correlation checks at regular intervals. This periodic processing enables real-time monitoring capability while maintaining manageable system complexity through systematic, rhythm-based operation rather than continuous complex computation.
Data Source
AI summary
A system and a method for processing multiple signals generated by sensors processing to identify and/or monitor physiological data of an individual (for example in healthcare system) or general statement of an environment, a predetermined space (for example a room, a machine, a building) or an object (for example in smart home system, environment monitoring system, fire prevention system or the like).