Health Data Template Alignment for Non-Uniform Wearable Inputs
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Solution Overview
Problem
Existing medical big data systems struggle to efficiently process and preprocess data from diverse portable and wearable health devices due to non-uniform data formats and frequencies, leading to data mismatches and inefficiencies in data utilization.
Innovation Solution
A data processing method and apparatus that utilizes eigenvector-based structural similarity analysis to align device-related data with a template format, performs interpolation and inverse distance weighting to supplement missing data, and assigns objects to groups based on scoring items, enhancing data uniformity and facilitating subsequent analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from diverse portable and wearable health devices are processed using existing medical big data systems, then data processing capability is provided, but data uniformity and processing efficiency deteriorate due to non-uniform data formats and frequencies
Solution Approach 1:
The patent transforms device-related data into template data by changing data format parameters. It uses eigenvector-based structural similarity analysis to map diverse data formats (different keywords, hierarchies, and structures) to a unified template format, enabling consistent processing of data from various health devices while maintaining the original information content.
Solution Approach 2:
The patent introduces template data as an intermediary between diverse device data formats and the processing system. The template serves as a standardization layer that mediates the transformation of non-uniform device data into a consistent format, facilitating efficient processing without losing device-specific information.
2Ease of operation
If data transformation to template format is performed using traditional methods, then data alignment is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent replaces traditional mechanical data alignment methods (manual mapping, rule-based transformation) with eigenvector-based structural similarity analysis. This computational approach uses linear algebra operations to automatically determine the best mapping between device data keywords and template keywords, significantly reducing processing time while improving alignment accuracy.
Solution Approach 2:
The patent transforms the data alignment problem from a combinatorial search problem into a mathematical optimization problem using eigenvectors. By representing keywords as eigenvectors and calculating structural similarity degrees through matrix operations, the system efficiently determines optimal mappings without exhaustive search, reducing computational time.
3Adaptability or versatility
If diverse data formats are processed without standardization, then data diversity is maintained, but data utilization efficiency and analysis quality deteriorate
Solution Approach 1:
The patent changes the data representation parameters by transforming diverse device data into a standardized template format while preserving the semantic meaning through eigenvector-based mapping. This allows the system to maintain adaptability to various device formats while achieving the uniformity needed for efficient processing and analysis.
Solution Approach 2:
The patent creates a universal template data structure that can accommodate data from multiple types of health devices. The template serves as a multi-functional framework that can process data from different devices with varying formats, hierarchies, and keywords while maintaining consistent processing capabilities across all data sources.
Data Source
AI summary
A data processing method, a data processing apparatus, and a health management apparatus. The data processing method includes: performing data processing on device-related data from a device associated with a health management apparatus. The data processing apparatus and the health management apparatus can improve the uniformity of memories provided to be associated with the health management apparatus.