Personalized Physiological Sleep Detection Using Initialization Baselines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current sleep detection and tracking methods lack the ability to accurately determine real-time sleep onset and wake-up times, relying on population-level statistics that fail to account for individual variations in physiological measures.
Innovation Solution
The use of user-specific physiological parameters measured during an initialization stage, correlated with real-time data to determine whether a user is awake or asleep, allowing for personalized adjustments in audio, visual, and haptic outputs to provide a customized experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If population-level statistics are used for sleep detection, then the system can provide general sleep tracking, but it cannot accurately determine real-time sleep onset or wake-up times due to individual variations in physiological measures
Solution Approach 1:
The system performs preliminary action by collecting and analyzing user-specific physiological data during an initialization stage before actual sleep detection begins. This pre-processing of individual baseline data enables subsequent real-time detection to be both accurate and personalized, resolving the contradiction between measurement precision and adaptability.
Solution Approach 2:
The system applies parameter changes by transitioning from fixed population-level statistical thresholds to dynamic, user-specific physiological parameters. By adjusting detection thresholds based on individual baseline measurements taken during initialization, the system achieves both high measurement precision and adaptability to individual variations.
2Measurement precision
If user-specific physiological parameters are measured and used for real-time detection, then accurate sleep onset and wake-up times can be determined, but the device complexity increases due to initialization stage and continuous monitoring requirements
Solution Approach 1:
The system applies segmentation by dividing the operation into two distinct stages: initialization stage for baseline data collection and steady-state stage for real-time detection. This segmentation reduces device complexity by organizing complex processing into manageable phases, while maintaining high measurement precision through personalized parameters established during initialization.
Solution Approach 2:
By performing preliminary data collection and analysis during the initialization stage, the system prepares user-specific parameters in advance. This preliminary action simplifies subsequent real-time detection operations, reducing the computational burden during steady-state operation while maintaining high accuracy.
3Ease of operation
If population-level statistics are used, then the system operation is simpler, but it provides inaccurate sleep tracking that cannot account for individual physiological variations
Solution Approach 1:
The system segments operation into initialization (performed once) and steady-state (ongoing) phases. This segmentation maintains ease of operation by automating the complex initialization process while providing simple, continuous real-time detection during steady-state, without sacrificing measurement precision through personalized parameters.
Solution Approach 2:
The system applies self-service by automatically collecting baseline data and generating personalized detection parameters during initialization without requiring user intervention. This maintains ease of operation while achieving high measurement precision through individualized physiological parameters.
Data Source
AI summary
Aspects of the present disclosure provide methods, apparatuses, and systems for accurately determining sleep and wake onset based on a user's personalized physiological parameters for sleep and wake. First, a user is determined to be asleep using population level data. Thereafter, sensor collected data is used to determine the user's distribution of values of a physiological parameter when the user is asleep. This distribution of values is then used, instead of population-level data, to determine the user is asleep in real-time. As a result, the content and interventions are provided to help users get back to sleep. Further, the described techniques allow more accuracy in determining sleep statistics which can guide recommended interventions and therapies.