Personalized Sleep Light and Sound Control via User Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for improving sleep quality, particularly during the falling asleep and awakening phases, are not adequately tailored to individual user preferences, activities, and environmental conditions, leading to suboptimal relaxation and wake-up experiences.
Innovation Solution
A system that uses a combination of a sensing mat, activity tracker, and central server to analyze sleep data and context information to provide personalized light and sound programs based on user-specific and group models, optimizing the falling asleep and awakening phases through tailored light and sound sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sleep optimization methods are used (ambient temperature, air humidity control), then general sleep conditions are improved, but the falling asleep and awakening phases are not adequately optimized
Solution Approach 1:
The system performs preliminary actions by analyzing user data (activity tracker information, sleep patterns, preferences) before the sleep phase begins, and before the awakening phase occurs. Light and sound programs are pre-configured based on daytime activities and user profiles, then automatically activated at appropriate times to optimize falling asleep and awakening experiences.
Solution Approach 2:
The system dynamically adapts light and sound programs based on real-time data from activity trackers, sleep sensors, and user feedback. The programs are not static but adjust according to individual user patterns, making the sleep optimization system versatile and personalized rather than conventional and one-size-fits-all.
2Ease of operation
If default or standard light and sound programs are used, then the system is simple to operate, but the relaxation phase and falling sleep phase are not appropriate for individual users
Solution Approach 1:
The system performs self-service by automatically analyzing user data from activity trackers and sleep sensors, then selecting and configuring appropriate light and sound programs without requiring manual user input. The system learns user preferences and daytime activities autonomously, providing personalized programs while maintaining ease of operation through automation.
Solution Approach 2:
The system incorporates feedback loops where user responses to light and sound programs (sleep quality data, wake-up mood assessments) are continuously analyzed. This feedback refines future program selections, allowing the system to become increasingly personalized over time while maintaining simple operation for the user.
3Ease of operation
If standard wake-up procedures are used, then the alarm function is simple, but the user mood at wakeup is not optimized
Solution Approach 1:
The system prepares the awakening phase in advance by analyzing sleep stage data and configuring gradual light and sound programs before the alarm triggers. Instead of abrupt wake-up, the system pre-conditions the user's environment with appropriate sensory stimuli to facilitate a positive wake-up mood while keeping the alarm function itself simple.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
Method for optimizing light and sound programs for a falling asleep phase (ST1) and for an awakening phase (ST2) of a first user (U01), in a system comprising, for the first user and other users, a bedside device (1), a bio parameter sensor (2,3), a smartphone (8), and additionally for all users a central server (5), with: /a1/ collecting, with regard to the first user U01, sleep data and sleep context data, said sleep data comprising at least light and sound program played for the falling asleep phase and for the awakening phase, bio parameters and sleep patterns sequence deduced therefrom, said sleep context data comprising at least previous daytime activity such as, sending this data to the central server (5), /a2/ repeat /a1/ for other users, /b1/ building a user-specific model of each user sleep behavior, /c/ comparing user-specific models to define groups of similar users, each group of users being allocated with a group meta-model with decision rules and preferred playlist of sound tracks, /d/ sending the group meta-model from the server to the bedside device or to the smartphone of the first user U01, /e/ displaying to the first user, using the group meta-model and in function of the time to go to sleep, a recommended list of light and sound programs or a particular light and sound program, namely a single choice, for the upcoming falling asleep phase and/or the next upcoming wakeup phase