Sleep Optimization Using Genetic and IoT Data
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Solution Overview
Problem
Individual sleep patterns vary significantly due to genetic and environmental factors, making it challenging to measure and optimize sleep quality and duration accurately, especially outside clinical settings, as existing methods like movement-based sensors can produce erroneous readings.
Innovation Solution
A computer-implemented method using genetic data and IoT device data to generate personalized sleep optimization plans, which includes correcting for genetic factors affecting sleep movement, such as the BTBD9 gene, to provide accurate and reliable sleep duration measurements and recommendations for improving overall health and well-being.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If movement-based sensors are used to measure sleep duration and quality, then sleep measurement can be performed outside clinical settings, but erroneous readings occur due to movement during sleep
Solution Approach 1:
The patent changes the measurement parameters by incorporating genetic data (specifically BTBD9 gene variants) to adjust the interpretation of movement patterns. Instead of treating all movement equally, the system modifies the threshold parameters for wakefulness detection based on individual genetic profiles, allowing accurate sleep measurement despite genetic variations in sleep-related movement
Solution Approach 2:
The patent introduces genetic data as an intermediary layer between raw movement sensor data and sleep classification. The genetic information acts as a mediator that contextualizes movement patterns, distinguishing between movement that indicates wakefulness and movement that is characteristic of certain genetic profiles during sleep, thereby resolving measurement errors
2Adaptability or versatility
If individualized sleep optimization is provided based on genetic data, then personalized sleep recommendations can be generated, but device and data processing complexity increases
Solution Approach 1:
The patent applies local quality by focusing on specific genetic markers (BTBD9 gene variants) rather than analyzing entire genomes. This targeted approach to genetic analysis provides personalized sleep recommendations while keeping the data processing requirements manageable and the system complexity controlled
Solution Approach 2:
The patent segments the sleep optimization process into distinct components: genetic data collection, movement pattern analysis, wakefulness period identification, and recommendation generation. This segmentation allows each component to be processed independently, reducing overall system complexity while maintaining personalization capabilities
Data Source
AI summary
Embodiments of the invention are directed to a computer-implemented method for generating a sleep optimization plan. A non-limiting example of the computer-implemented method includes receiving, by a processor, genetic data for a user. The method also includes receiving, by the processor, Internet of Things (IoT) device data for the user. The method also includes generating, by the processor, a sleep duration measurement for the user based at last in part upon the IoT device data. The method also includes generating, by the processor, a sleep optimization plan for the user based at least in part upon the genetic data.