Millimeter-Wave Radar Sleep Monitoring for Contactless Apnea Detection
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Solution Overview
Problem
Traditional sleep disorder diagnosis and monitoring methods, such as polysomnography, are cumbersome, costly, and limited in availability, necessitating the development of contactless and convenient alternatives for assessing sleep parameters.
Innovation Solution
A system utilizing millimeter-wave radar combined with machine learning to estimate sleep parameters by analyzing reflected electromagnetic waves for cardiac and pulmonary activity, enabling accurate detection of sleep apnea events and stages without physical sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography is used for sleep disorder diagnosis, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent extracts and isolates only the essential physiological signals (breathing patterns, body movements) needed for sleep disorder detection, discarding the complex array of sensors and measurements used in traditional polysomnography. This allows maintaining diagnostic accuracy while dramatically simplifying the system.
Solution Approach 2:
The patent replaces the mechanical and electrical sensor-based polysomnography system with a radar-based electromagnetic sensing system. This substitution eliminates the need for physical contact with the patient while maintaining the ability to detect physiological parameters.
2Measurement precision
If polysomnography is used for sleep disorder diagnosis, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The radar system operates autonomously without requiring patient cooperation or active participation. The system automatically detects and analyzes physiological signals during natural sleep, eliminating the need for patients to adapt to laboratory settings or wear cumbersome equipment.
Solution Approach 2:
The patent removes all elements that complicate patient interaction and setup procedures, creating a passive monitoring system that requires minimal user intervention while maintaining diagnostic accuracy.
3Ease of operation
If contactless radar monitoring is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent combines radar signal processing with machine learning algorithms to create a composite analytical system. This integration compensates for the inherent limitations of contactless sensing by using multiple signal processing techniques and computational methods to extract accurate physiological information.
Solution Approach 2:
The patent replaces direct physical measurement with electromagnetic wave-based detection, using sophisticated signal processing to bridge the gap between contactless sensing and accurate physiological parameter extraction.
4Reliability
If traditional polysomnography is used, then reliability is improved, but device complexity worsens
Solution Approach 1:
The patent extracts only the critical physiological parameters needed for reliable sleep disorder diagnosis, eliminating redundant sensors and measurements while maintaining diagnostic confidence through focused, targeted detection of key indicators.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and continuous monitoring of sleep apnea events and stages in a home environment, overcoming the limitations of traditional methods by providing a convenient and reliable assessment of sleep quality.
Implementation Method 1
receiving a millimeter-wave reflection radar signal reflected from a monitored subject
Implementation Method 2
analyzing reflected electromagnetic waves for cardiac and pulmonary activity
Data Source
AI summary
A system and method for monitoring sleep parameters, the method including during a model training phase: receiving millimeter-wave reflection radar signals from reference subjects, extracting in-phase and quadrature components, deriving displacement signals reflecting body micromovements from cardiac and pulmonary activity, segmenting displacement signals into reference segments, forming a training dataset with segments labeled with measured sleep data, and applying machine learning to generate a sleep parameters estimation model; and during a subject monitoring phase: receiving millimeter-wave reflection radar signals from a monitored subject, extracting signal components, deriving displacement signals reflecting body micromovements, segmenting into monitored segments, applying the estimation model to estimate sleep parameters, determining overall sleep duration, and determining a sleep parameters index based on estimated parameters and duration.