Personalized Biometric Signal Processing for Accuracy
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Solution Overview
Problem
Existing electronic devices use generalized models to estimate biometric information, which can lead to inaccurate results due to individual variations in body characteristics and biometric signals, failing to provide precise biometric information such as stress intensity.
Innovation Solution
An electronic device with a sensor module, memory, and processor that detects biometric signals, determines a stable state, extracts representative values, and updates reference values based on predetermined conditions to provide personalized biometric information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a generalized model is used for estimating biometric information, then the device complexity is reduced and ease of operation is improved, but the measurement precision and reliability of biometric information deteriorate due to individual variations in body characteristics
Solution Approach 1:
The system performs preliminary actions by collecting biometric signals during stable states and establishing personalized reference values before actual biometric estimation. This advance preparation enables accurate individualized modeling without increasing operational complexity, as the reference values are pre-computed and stored for later use in biometric information estimation
Solution Approach 2:
The system uses the user's own biometric signals to automatically generate personalized reference values without requiring external calibration data or manual intervention. The device self-adjusts to individual characteristics by processing its own collected data, eliminating the need for complex external calibration procedures while improving measurement precision
2Reliability
If generalized biometric models are used, then the ease of manufacture and device complexity are reduced, but the reliability of biometric information deteriorates due to inability to account for individual body characteristic variations
Solution Approach 1:
The system transitions from static generalized models to dynamic personalized models by continuously updating reference values based on detected stable states. This dynamic adaptation to individual characteristics improves reliability while maintaining implementation feasibility through automated detection and update mechanisms that don't require complex manual configuration
3Measurement precision
If personalized reference values are established through continuous monitoring and stable state detection, then the measurement precision of biometric information is improved, but the loss of time for data collection and processing increases
Solution Approach 1:
The system uses periodic monitoring at predetermined time intervals to detect stable states and collect biometric signals. This periodic approach balances measurement precision with time efficiency by sampling at optimal intervals rather than continuous monitoring, reducing time loss while still capturing sufficient data for accurate personalized reference value establishment
Solution Approach 2:
The system performs preliminary data collection during detected stable states before actual biometric estimation is needed. By accumulating reference data in advance during stable periods, the system reduces the time required during critical measurement phases, as the reference values are already established and ready for immediate use
Data Source
AI summary
An electronic device for providing biometric information is provided. The electronic device includes a sensor module, a memory, and a processor electrically connected to the sensor module and the memory. The processor obtains a biometric signal from the sensor module at a predetermined time interval, determines whether a user is in a first state on the basis of the obtained biometric signal, in case the user is in a first state, obtains a representative value for a respective of the at least one biometric signal, defines the obtained representative value for the respective of the at least one biometric signal as a candidate reference value for a corresponding biometric signal, determines a candidate reference value satisfying a predetermined condition as a first reference value for the corresponding biometric signal, and updates a second reference value previously configured for the corresponding biometric signal on the basis of the first reference value.