AI Sleep State Determination Using Biometric Signal Processing
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Solution Overview
Problem
Current methods for determining sleep states and diagnosing sleep diseases rely on manual analysis of bio-signals by professionals, which is time-consuming, costly, and prone to inaccuracies due to noise in large datasets.
Innovation Solution
An electronic device equipped with sensors to collect biometric data such as oxygen saturation and electrocardiogram (ECG) signals, which are then processed and input into an AI model trained to determine sleep states, thereby automating the diagnosis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of bio-signals by professionals is used to determine sleep states and diagnose sleep diseases, then measurement precision and reliability are improved, but loss of time and device complexity increase
Solution Approach 1:
The patent replaces manual analysis by professionals with an automated AI-based system. The AI model processes bio-signals (oxygen saturation, ECG data) automatically to determine sleep states and diagnose sleep diseases, eliminating the need for manual professional analysis while maintaining accuracy.
Solution Approach 2:
The system enables self-service sleep state determination through automated AI processing. The AI model independently analyzes bio-signals without requiring professional intervention, allowing the system to serve itself in determining sleep states and diagnosing conditions.
2Measurement precision
If manual analysis of bio-signals by professionals is used to determine sleep states and diagnose sleep diseases, then measurement precision and reliability are improved, but cost increases
Solution Approach 1:
The patent replaces manual professional analysis with automated AI processing, significantly reducing the cost of sleep state determination. The AI model processes bio-signals without requiring professional personnel, making the service more cost-effective while maintaining diagnostic accuracy.
3Device complexity
If automatic determination of sleep state using raw biometric information is used, then device complexity is reduced, but measurement precision deteriorates due to noise data
Solution Approach 1:
The patent applies preliminary action by pre-processing and cleaning the biometric data before it is input into the AI model. The system processes and prepares the raw bio-signals (oxygen saturation, ECG data) in advance to remove noise and ensure data quality, which then enables accurate sleep state determination by the AI model.
4Measurement precision
If large amount of biometric information is used for sleep state determination, then measurement precision may be improved, but loss of time and storage space requirements increase
Solution Approach 1:
The patent extracts and selects only the essential features from the large amount of biometric information. The AI model processes and extracts key parameters (such as peak points, time intervals, and pattern characteristics) from the raw bio-signals, filtering out redundant and noisy data to achieve accurate sleep state determination efficiently.
Data Source
AI summary
According to various embodiments, the electronic device includes a communication device, a storage device storing a first artificial intelligence model trained for sleep state determination, and at least one processor, wherein the processor acquires first initial biometric information regarding oxygen saturation and second initial biometric information regarding ECG data of a sleeping user via a sensor device, the processor converts this initial information into multidimensional first and second biometric information, identifies ECG peak points and intervals, and inputs these data into the AI model to determine the user's sleep state, and the sleep state information is then transmitted to a display device connected to the electronic device for output.