Sensor Data Outlier Detection via Signal Dynamics Analysis
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Solution Overview
Problem
Conventional systems face challenges in effectively identifying and prioritizing critical information from sensor data, particularly in determining outlier significance across various types of sensor data, which is essential for applications like ECG data analysis to detect heart conditions, due to differences in signal dynamics and data processing requirements.
Innovation Solution
A method and system that determine signal dynamics using statistical and signal processing features, select appropriate outlier classes based on data characteristics, and employ specific outlier detection methods to calculate an information score for each outlier, enabling the identification of outliers with the highest information content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional outlier detection techniques are used on sensor data, then the detection process is simple, but the accuracy of identifying critical information is low due to not considering signal dynamics
Solution Approach 1:
The system performs preliminary analysis of signal dynamics characteristics (stationary, periodic, random, non-periodic) before applying outlier detection. This preliminary classification enables selection of appropriate detection techniques tailored to each signal type, improving detection accuracy without uniformly increasing complexity across all data streams
Solution Approach 2:
The outlier detection process is segmented into multiple specialized techniques: point-based detection for individual outliers, contextual detection for patterns, and collaborative detection for multi-sensor data. Each segment addresses specific signal dynamics characteristics, allowing accurate detection while managing complexity through modular processing
2Reliability
If all sensor data is transmitted with high priority, then no critical information is lost, but communication cost and energy consumption increase
Solution Approach 1:
The system applies different transmission priorities and reliability levels to different data points based on their individual information content and outlier status. Critical outliers receive high priority transmission with enhanced reliability, while normal data uses standard transmission, optimizing the balance between reliability and energy consumption on a local data-point basis
3Loss of information
If comprehensive outlier detection is performed on all sensor data streams, then all critical information is captured, but processing time and computational resources increase
Solution Approach 1:
The system dynamically adjusts the outlier detection process based on signal dynamics characteristics. For stationary signals, simpler detection methods are applied; for periodic or random signals, more sophisticated methods are used. This dynamic adaptation ensures comprehensive critical information capture while minimizing processing time through optimized algorithm selection
Solution Approach 2:
The system performs partial outlier detection by focusing computational resources on detecting outliers in signals with higher information content or greater variability. Not all data streams undergo the same level of scrutiny - the system applies detection intensity proportional to the potential information value, reducing overall processing time while capturing critical outliers
Data Source
AI summary
The present subject matter discloses a system and a method for identifying information from sensor data in a sensor agnostic manner. The system may receive sensor data provided by a sensor and may determine statistical features of the sensor data. The system may determine signal dynamics of the sensor data based on at least one of the statistical features, signal processing features, and a data distribution model. The system may select at least one outlier class based on the signal dynamics, number of streams of the sensor data, and dimensions of the sensor data. The system may select at least one outlier detection method associated with an outlier class for detecting outliers in the sensor data. The system may determine information content of the sensor data based on the outliers, the signal dynamics, the statistical features, and information theoretic features, and similarity or dissimilarity measure.