Wakefulness Level Determination Using Heartbeat Signal Correlation
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Solution Overview
Problem
Conventional technologies face difficulties in determining a subject's wakefulness level due to the challenge of setting a scale without heartbeat signals obtained when the subject is not fully awake, and the incompatibility of biometric information used across different technologies.
Innovation Solution
A wakefulness level determination device that calculates feature values from heartbeat signals, estimates values for when the subject is not fully awake based on correlations, and sets an index range for wakefulness levels, allowing for real-time determination without prior signals from an unawake state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional technology uses heartbeat signals to set a scale for wakefulness level determination, then the scale can be established using biometric information, but it becomes difficult to set the scale because heartbeat signals obtained when the subject is not fully awake are hard to obtain
Solution Approach 1:
The patent applies preliminary action by establishing a correlation model between wakeful and non-wakeful states using only wakeful heartbeat signals beforehand. The scale setting unit pre-calculates the relationship between heartbeat feature values and wakefulness levels during wakeful states, creating a predictive model that can later estimate non-wakeful states without requiring actual non-wakeful signal data.
Solution Approach 2:
The patent introduces an intermediary approach by using a correlation-based estimation mechanism. Instead of directly measuring non-wakeful heartbeat signals (which are hard to obtain), the system uses the established correlation between wakeful and non-wakeful states as an intermediary to infer wakefulness levels. The scale setting unit acts as this intermediary by translating wakeful signal patterns into predictive scales for non-wakeful conditions.
2Adaptability or versatility
If different biometric information types are used in conventional technologies, then various wakefulness indicators can be measured, but the technologies become incompatible and difficult to combine
Solution Approach 1:
The patent applies universality by designing a scale setting unit that can handle multiple types of biometric information (heartbeat signals, blood pressure, respiration) through a unified correlation-based framework. The system establishes a common scale structure that can accommodate different biometric data types, allowing the same wakefulness determination technology to work across various physiological indicators without requiring separate processing systems for each type.
3Measurement precision
If heartbeat signals from unawake states are required to set the scale, then accurate wakefulness level determination can be achieved, but the system cannot determine wakefulness level in real time without prior unawake signals
Solution Approach 1:
The patent resolves this contradiction by performing preliminary action during wakeful states. The scale setting unit pre-establishes the correlation between heartbeat feature values and wakefulness levels using only wakeful signals. This preliminary modeling enables the system to later determine wakefulness levels in real-time by applying the pre-established correlation to current wakeful signals, eliminating the need to wait for or obtain non-wakeful signals beforehand.
Data Source
AI summary
A wakefulness level determination device includes a processor; and a memory. The processor executes: calculating a first feature value from a heartbeat signal of a subject; estimating, from the first feature value calculated at the calculating, a feature value of the subject when not fully awake on the basis of a correlation between a feature value of a heartbeat signal of a person fully waking and a feature value of a heartbeat signal of a person not fully waking; setting, as an index of a wakefulness level, a range from the first feature value calculated at the calculating to the feature value estimated at the estimating; calculating a second feature value from a heartbeat signal of the subject; and determining a wakefulness level of the subject by comparing the second feature value with the index of the wakefulness level that is set at the setting.