Medical Sensor Data Annotation via Characteristic Functions
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Solution Overview
Problem
Existing techniques face challenges in efficiently processing and analyzing large volumes of sensor data, particularly in medical contexts, where representing and annotating data effectively is crucial for accurate analysis and decision-making.
Innovation Solution
The system represents sensor data using characteristic functions, which are stored as binary decision diagrams, allowing for efficient storage and querying, and enables annotation by transforming Boolean functions into arithmetic functions to determine equivalence through hash code calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data is stored in traditional formats, then data can be retrieved, but storage efficiency and querying speed deteriorate with large data volumes
Solution Approach 1:
The patent transforms sensor data from traditional time-series formats into a mathematical function representation (characteristic function). This parameter change allows the data to be compressed into a compact mathematical model that can represent large volumes of sensor readings efficiently, resolving the contradiction between processing efficiency and data volume.
Solution Approach 2:
Instead of storing actual sensor readings, the patent creates a mathematical copy (characteristic function) that reproduces the essential information. This copying approach allows rapid querying and analysis without handling the full volume of original data, improving productivity while reducing the effective data volume that must be processed.
2Measurement precision
If detailed sensor data is retained for accurate analysis, then measurement precision is improved, but data complexity increases
Solution Approach 1:
The patent extracts the essential characteristics of sensor data into a mathematical function representation, separating the critical information from the raw data volume. This extraction maintains measurement precision by preserving the characteristic behavior of the sensor data while removing redundant information that contributes to complexity.
Solution Approach 2:
By transforming the data representation from discrete time-series points to a continuous mathematical function, the patent changes the parameter structure fundamentally. This transformation maintains the precision needed for accurate analysis while presenting the data in a more manageable and less complex form.
3Reliability
If data is annotated with medical annotations for better decision-making, then reliability is improved, but processing time increases
Solution Approach 1:
The patent performs annotation processing on the compact mathematical representation of sensor data rather than on the full raw dataset. This preliminary action on the compressed form maintains reliability by preserving the essential information needed for accurate medical annotations while significantly reducing the time required for processing.
Data Source
AI summary
According to certain embodiments, a set of samples of sensor data is accessed. The set of samples records medical measurements taken by one or more medical sensors. A characteristic function is generated from the set of samples. The characteristic function indicates whether a given sample is a member of the set of samples. One or more samples of the set of samples that are associated with a given medical annotation are identified according to the characteristic function.