Sleeping Data Attribution via Feature Similarity Comparison
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Solution Overview
Problem
The accuracy of sleeping data attribution is reduced when a sleeping monitoring device is used by multiple users without timely updates in user binding, leading to incorrect attribution of data.
Innovation Solution
A method and apparatus for processing sleeping data that involves acquiring data from the device, extracting features such as respiration and heartbeat patterns, performing similarity comparisons with standard user features, and using the comprehensive feature similarity to accurately attribute the data to the correct user, thereby improving data attribution accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sleeping monitoring device is used by multiple users without timely updates in user binding, then device versatility is improved, but data attribution accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by extracting sleeping features (respiration, heartbeat, body position) from monitoring data and comparing them with pre-stored user standard features before final data attribution. This preliminary feature analysis enables accurate user identification even when device binding is not updated, resolving the contradiction between multi-user versatility and attribution accuracy.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing extracted sleeping features with stored user profiles and using the similarity results to determine correct data attribution. The feedback loop ensures that even without timely user binding updates, the system can accurately attribute data by referencing historical feature patterns, thus maintaining accuracy while supporting multiple users.
2Measurement precision
If sleeping features are extracted and compared with standard features, then data attribution accuracy is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the most critical sleeping features (respiration rate, heartbeat characteristics, body position) from the monitoring data for comparison, rather than processing all available data. This selective extraction reduces computational complexity while maintaining attribution accuracy by focusing on the most discriminative features.
Solution Approach 2:
The system applies different processing depths to different features based on their discriminative value. Critical features like heartbeat patterns receive more detailed analysis, while less important features are processed more simply. This local quality approach optimizes the balance between processing complexity and attribution accuracy.
Data Source
AI summary
Some embodiments of the present disclosure provide a method and an apparatus for processing sleeping data, a computer device, a program and a medium, which relates to the technical field of computers. The method includes: acquiring sleeping data collected by a sleeping monitoring device; extracting a sleeping feature in the sleeping data; performing similarity comparison to a standard feature of a user and the sleeping feature, to obtain a comprehensive feature similarity; and on the condition that the comprehensive feature similarity satisfies a similarity requirement, using the sleeping feature as a target sleeping feature of the user.