IoT Health Data Verification Using Biological Models
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Solution Overview
Problem
Existing health data management systems in Internet-of-Things health detection scenarios face challenges in ensuring data integrity, security, and accuracy due to public access and network vulnerabilities, leading to potential errors and inconsistencies in health data collection and transmission.
Innovation Solution
A health data management method and apparatus that includes identity verification, data filtering, and a biological model verification process to ensure accurate health detection results, utilizing a health management server to receive, preprocess, and analyze health detection data from Internet-of-Things terminals, incorporating sub-models for various health parameters and considering environmental and physical factors to enhance data reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If health data is collected from public Internet-of-Things terminals, then data collection scope and indicators are increased, but data integrity and security are compromised
Solution Approach 1:
A health management server is introduced as an intermediary between Internet-of-Things terminals and users. The server receives health detection data from multiple terminals, performs centralized verification against biological models, and manages data security. This mediator resolves the contradiction by enabling broad data collection while maintaining integrity through centralized control and verification mechanisms.
Solution Approach 2:
The system performs preliminary actions by pre-establishing biological models for users before data collection occurs. These models contain expected physiological ranges and patterns. When data is collected from Internet-of-Things terminals, it is automatically verified against these pre-established models, ensuring data integrity is maintained from the outset rather than requiring post-collection verification.
2Measurement precision
If data filtering and verification processes are implemented, then data accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The verification system performs self-service by automatically comparing collected health data against pre-established biological models without requiring manual intervention. The system autonomously identifies anomalies, verifies data consistency, and flags suspicious readings. This automation maintains high data accuracy while minimizing the complexity burden on operators, as the system handles verification tasks independently.
3Reliability
If multiple detection sub-models are verified with reference data, then model reliability is enhanced, but verification complexity increases
Solution Approach 1:
The biological model is segmented into multiple detection sub-models, each responsible for specific health parameters (e.g., cardiovascular, respiratory, metabolic). Each sub-model can be independently verified against relevant reference data. This segmentation allows comprehensive model verification without overwhelming complexity, as each sub-model's verification can be handled separately and systematically.
Data Source
AI summary
The present disclosure provides a health data management method, a health data management apparatus, an electronic device, and a readable storage medium. The health data management method is applied to a health management server in communication with an Internet-of-Things health detection terminal, and includes: receiving health detection data associated with a target user from the Internet-of-Things health detection terminal; establishing a biological model of the target user in accordance with the health detection data; and verifying the biological model, and generating a health detection result of the target user in accordance with the verified biological model.