Health And Performance Data Normalization Across Diverse Sources
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Solution Overview
Problem
Current health and physical performance monitoring systems operate independently, creating data fragmentation that hinders comprehensive analysis, decision-making, and personalized care due to disparate data formats and lack of integration, which impedes large-scale research and predictive modeling.
Innovation Solution
A computerized system for collecting and normalizing health and physical performance data from diverse sources, using normalization modules to translate data into a standardized format, integrated with a central data store for unified analysis and visualization, incorporating AI and machine learning for predictive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple independent sources with proprietary formats, then data quantity and coverage increase, but data integration and standardization become difficult
Solution Approach 1:
The patent implements a normalization module that acts as an intermediary between diverse data sources and the central database. This module translates proprietary formats from different devices (wearables, medical devices, fitness equipment) into a standardized central format, enabling integration without requiring changes to the original data sources or creating direct complex connections between them.
Solution Approach 2:
The system architecture is segmented into independent components: data collection layer (multiple sources), normalization layer (translation module), storage layer (central database), and application layer (analysis tools). This segmentation allows each component to operate independently with its own format while maintaining overall system integration through the normalization interface.
2Reliability
If data is stored in proprietary formats across multiple platforms, then each platform maintains its own data integrity, but consolidated analysis and research become hindered
Solution Approach 1:
The normalization module serves as a mediator that preserves the integrity of source data while enabling versatile analysis. It creates a standardized representation in the central database without altering the original proprietary formats in the source systems, allowing both data integrity maintenance and consolidated analysis.
Solution Approach 2:
The system creates standardized copies of data from proprietary sources for storage and analysis in the central database, while the original data remains unchanged in its source systems. This copying approach enables versatile analysis of aggregated data without compromising the reliability or integrity of the source data.
3Ease of operation
If independent monitoring systems operate separately, then system simplicity and ease of operation are maintained, but comprehensive health and performance insights are limited
Solution Approach 1:
The patent merges data from multiple independent monitoring systems into a unified central database through standardized formatting. This combining approach enables comprehensive health and performance insights by aggregating information from wearables, medical devices, and fitness equipment while maintaining the operational simplicity of each individual system.
Data Source
AI summary
A computerized system for collecting and normalizing health and physical performance data comprises: a central data store; multiple data sources communicating via a global network, each providing data in different structured formats; normalization modules translating data related to health, wellness, performance data and the like into a central format; and an end user system. The system includes a first data source associated with an exercise machine and a second data source, each with corresponding normalization modules. These modules translate data into a central format for storage in the central data store. The end user system retrieves translated data, processes it, and provides results to users. The system may include a cloud storage for data aggregation and a customized interface for different industries. This unified approach enables comprehensive health and fitness data analysis, supporting personalized recommendations and research applications through artificial intelligence and machine learning algorithms.