Ai-based contactless sleep analysis method and real-time sleep environment adjustment method
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sleep analysis methods using wearable devices face challenges such as discomfort causing deterioration in sleep quality, periodic management requirements, and limitations in analyzing sleep without proper contact or when multiple users share a sleeping space.
Innovation Solution
An AI-based non-contact sleep analysis system that utilizes a smartphone and smart home appliances to collect and analyze sleep sound information in real-time, providing a customized sleep environment based on the analyzed data without the need for wearable devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wearable devices are used for sleep analysis, then sleep analysis can be performed, but sleep quality deteriorates due to discomfort and periodic management is required
Solution Approach 1:
The patent extracts the sleep analysis function from wearable devices and relocates it to a server-based system. The wearable device only needs to collect basic data (heart rate, body temperature, movement), while the actual analysis is performed remotely on a server, eliminating the need for continuous wearing and periodic charging while maintaining analysis capability
Solution Approach 2:
The patent introduces a server as an intermediary between the wearable device and the user. The server performs sleep analysis by integrating multiple data sources (wearable device data, smartphone data, environmental data) and provides results to the user, eliminating the need for the wearable device to continuously process data on-device
2Measurement precision
If wearable devices are used for sleep analysis, then individual sleep can be monitored, but analysis is hindered when multiple users share the same sleeping space
Solution Approach 1:
The patent segments the sleep monitoring approach by using multiple independent data collection points (wearable device on one user, smartphone on another user, environmental sensors) that can be individually associated with specific users. The server then integrates these segmented data sources to provide both individual and collective sleep analysis
Solution Approach 2:
The patent creates a universal sleep analysis system that can handle both single-user and multi-user scenarios. The same server-based platform processes data from various sources (wearable devices, smartphones, environmental sensors) and can generate individualized reports for each user or aggregated reports for the household, making it adaptable to different user configurations
3Quantity of substance
If conventional sleep analysis methods are used, then basic sleep parameters can be measured, but wake time detection accuracy is limited due to small differences in HRV and brain wave changes
Solution Approach 1:
The patent merges multiple data sources (heart rate variability from wearable device, body temperature from wearable device, movement data from wearable device, ambient light from smartphone, environmental data from sensors) to detect wake time. By combining these complementary signals, the system overcomes the limitation of relying on a single parameter with small changes
Solution Approach 2:
The system continuously monitors multiple parameters and uses feedback from each data source to refine wake time detection. When multiple indicators (increased movement, temperature change, light exposure) simultaneously suggest wakefulness, the system confirms wake time with higher confidence, improving detection accuracy
Data Source
AI summary
An AI-based contactless sleep analysis system and method are disclosed. The sleep analysis system comprises: a smartphone that downloads a sleep analysis application from a server, collects and transmits sleep sound information of a user in real time to the server, and receives a report of AI-trained sleep analysis results from the server; and at least one smart home appliance that is spaced apart and located around the user, simultaneously collects and transmits the sleep sound information to the smartphone, and provides a customized sleep environment based on the analysis results to the user who is response to a control of the smartphone.