Bed Sensors for Long COVID State Classification
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Solution Overview
Problem
Current technologies lack effective methods to monitor and classify the physical states of individuals, particularly for chronic conditions like long COVID, without requiring active user involvement or specialized devices, and struggle to provide timely and accurate health insights.
Innovation Solution
A bed system equipped with sensors that monitor physical phenomena such as sleep duration, breathing rate, and heart rate, generating feature vectors to classify the sleeper's physical state into categories like healthy or not healthy, and providing recovery recommendations based on medically-expert rules, with the ability to schedule medical tests and track illness progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are integrated into the bed to monitor physical phenomena, then health monitoring capability is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensing functions (motion detection, respiratory monitoring, cardiac monitoring) into a single bed system, integrating these capabilities into the mattress structure rather than requiring separate devices. This merging approach improves health monitoring capability while managing device complexity through consolidation.
Solution Approach 2:
The bed system is designed to perform multiple functions: it serves as both a sleeping surface and a health monitoring device. The sensors are configured to detect various physical phenomena (motion, respiration, cardiac activity) simultaneously, making the system universal and multi-functional, thereby improving health monitoring without requiring multiple separate devices.
2Measurement precision
If automated classification and analysis systems are implemented, then health insight accuracy is improved, but computing resource requirements increase
Solution Approach 1:
The system performs preliminary classification of sleep data into discrete states (awake, light sleep, deep sleep, REM) using predefined criteria. This preliminary action organizes raw data into structured categories before more complex analysis, improving health insight accuracy while reducing the computational resources needed for subsequent processing by working with pre-organized data.
Solution Approach 2:
The system automatically processes and classifies health data without requiring external medical expertise for every analysis. The automated classification algorithms independently evaluate sensor data and generate health insights, reducing the need for continuous human intervention and lowering ongoing computing resource requirements while maintaining accuracy.
3Loss of time
If continuous monitoring throughout the sleep session is performed, then health state detection timeliness is improved, but energy consumption increases
Solution Approach 1:
The system performs monitoring at periodic intervals throughout the sleep session rather than continuously. Sensors collect data at defined sampling rates and the system evaluates health states at regular checkpoints, ensuring timely detection of health changes while reducing energy consumption by avoiding constant active processing. This periodic approach balances timeliness with energy efficiency.
Data Source
AI summary
A bed has a mattress. One or more sensors are configured to sense one or more physical phenomena of a sleeper on the bed and generate data signals based on the sensed physical phenomena; and send, to a computing system, the data signals. A computing system comprising one or more processors and computer memory. The computing system is configured to: receive the data signals; generate, from data signals of a sleep-session of the sleeper, a feature vector of features, each feature having a feature value that represents one of the physical phenomena; and classify the sleeper into a classified physical state of long COVID for the sleep session based on the feature vector.