Bed with features for determination of insomnia risk
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Solution Overview
Problem
Current bed technologies lack the ability to effectively monitor and respond to physiological data to predict and mitigate insomnia risk, failing to provide personalized and automated solutions for improving sleep quality.
Innovation Solution
A system comprising sensors and a computing system that generates an insomnia-risk metric using machine learning analysis of physiological data such as respiration rate, heart rate, motion, and sleep quality, which engages automated peripheral devices to create a conducive sleep environment based on a daily schedule.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors and computing systems are integrated into the bed to monitor physiological data and predict insomnia risk, then sleep quality monitoring and prediction capability is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensors (motion sensors, temperature sensors, humidity sensors) and computing systems into an integrated bed structure. The sensors are embedded within the mattress and bedding components, merging the monitoring functionality with the existing bed architecture rather than adding separate standalone devices.
Solution Approach 2:
The bed system performs multiple functions: it monitors physiological data (respiration rate, heart rate), tracks sleep patterns (sleep duration, sleep quality), predicts insomnia risk, and provides environmental control. This multi-functional approach consolidates what would otherwise require separate devices into a single integrated system.
2Reliability
If automated peripheral devices are engaged based on insomnia-risk metric to create conducive sleep environment, then sleep quality improvement is achieved, but device complexity and automation extent increase
Solution Approach 1:
The system predicts insomnia risk in advance by analyzing physiological data and sleep patterns before the actual sleep occurrence. This preliminary assessment allows the system to proactively adjust environmental conditions (temperature, humidity, lighting) before sleep problems manifest, preventing rather than just reacting to issues.
Solution Approach 2:
The system continuously monitors physiological data and sleep patterns, compares them against established norms and historical data, and uses this feedback to dynamically adjust environmental controls. The insomnia-risk metric serves as a feedback signal that triggers automated adjustments to create optimal sleep conditions.
Data Source
AI summary
One general aspect includes a bed having a mattress. The system also includes one or more sensors configured to: sense physiological phenomenon of a user of the bed and generate one or more data streams based on the sensing of the physiological phenomenon of the user. The system also includes a computing system that may include at least one processor and computer memory, the computing-system configured to: receive the one or more data streams and generate, using the one or more data streams, an insomnia-risk metric for the user reflective of risk that the user will or is experiencing symptoms may include with insomnia.