Custom Sleep Age Estimation via Neural Activity Stimulation
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Solution Overview
Problem
Traditional techniques for measuring neural activity during sleep are limited in their ability to efficiently and effectively tailor custom sleep parameters for individuals.
Innovation Solution
The implementation of systems and methods that generate custom sleep age profiles by using processors to create sleep models based on reference data, estimate sleep age from neural activity measurements, and generate stimulation parameters to modify sleep age.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional techniques are used to measure neural activity, then measurement capability is provided, but the ability to customize sleep parameters for individuals is limited
Solution Approach 1:
The system changes the parameter of sleep age estimation to enable customization. By estimating sleep age from neural activity patterns and using this to adjust stimulation parameters, the system achieves individualized sleep optimization without requiring completely new measurement technologies, thus improving adaptability while managing complexity through parameter transformation rather than structural expansion
Solution Approach 2:
The system implements feedback by continuously monitoring neural activity, estimating sleep age, and adjusting stimulation parameters accordingly. This closed-loop approach enables dynamic customization of sleep parameters based on real-time individual responses, improving adaptability while managing complexity through automated feedback-driven adjustment rather than manual configuration
2Reliability
If sleep age estimation is performed to customize sleep parameters, then sleep quality improvement is achieved, but processing time is required
Solution Approach 1:
The system performs preliminary action by pre-establishing sleep age estimation models and stimulation parameter frameworks before actual sleep optimization is needed. This allows the system to quickly estimate sleep age and generate appropriate stimulation parameters during sleep periods without requiring extensive real-time processing, thus improving sleep quality while minimizing processing time through preparatory model construction
Solution Approach 2:
The system substitutes complex real-time computational analysis with pre-computed models and simplified estimation algorithms. By replacing heavy mechanical processing with optimized mathematical models that can rapidly estimate sleep age from neural patterns, the system achieves high reliability sleep quality improvement while reducing processing time through algorithmic optimization rather than brute-force computation
Data Source
AI summary
Provided are systems, methods, and devices for implementation of custom sleep age profiles. Methods include generating at least one sleep model based, at least in part, on reference data, the at least one sleep model being configured to identify an estimated sleep age based on an input. Methods further include receiving measurement data comprising data values representing measurements of neural activity of at least one user, and generating an estimated sleep age of the at least one user based, at least in part, on the at least one sleep model and the received measurement data. Methods also include generating a plurality of stimulation parameters based, at least in part, on the estimated sleep age, the plurality of stimulation parameters being configured to modify the estimated sleep age of the at least one user.