Millimeter-Wave Sleep Disruption Detection via Cross-Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sleep disruption monitoring methods are invasive, complicated to set up, and can disrupt natural sleeping patterns, leading to safety issues and discomfort, necessitating a non-invasive and contactless solution.
Innovation Solution
A wireless signal-based system utilizing millimeter-wave technology on 5G devices, which transmits and receives signals to identify movements and classify sleep states using cross-correlation and a Hidden Markov Model, enabling fine-grained disruption monitoring without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing sleep disruption monitoring methods are used, then sleep disruptions can be monitored, but the monitoring is invasive and can disrupt natural sleeping patterns
Solution Approach 1:
The patent replaces mechanical contact-based sensing systems with electromagnetic wave-based millimeter-wave radar technology. The system uses mmWave signals to detect sleep disruptions through wireless reflection off the subject's body, eliminating the need for physical contact sensors that cause discomfort and disrupt natural sleep patterns while maintaining detection accuracy.
Solution Approach 2:
The patent introduces millimeter-wave electromagnetic waves as an intermediary medium between the monitoring system and the sleeping subject. These waves reflect off the subject's body to carry information about movements and posture changes, enabling non-contact monitoring that does not interfere with the subject's natural sleep state.
2Object-affected harmful factors
If contactless monitoring is implemented, then subject comfort is improved, but measurement accuracy may deteriorate
Solution Approach 1:
The patent employs signal processing parameter transformations including Fast Fourier Transform (FFT) to convert time-domain reflected signals into frequency-domain spectral features. Additional parameters such as power spectral density, center frequency, and spectral entropy are calculated to enhance the discriminative power of subtle movement patterns, maintaining high detection accuracy in contactless mode.
Solution Approach 2:
The patent creates a virtual model of the sleeping subject's movements by analyzing reflected millimeter-wave signals. The system reconstructs motion information from wireless signal reflections, generating a digital representation of sleep patterns that accurately captures toss-turn events without requiring physical contact with the subject.
3Measurement precision
If additional hardware is added to improve monitoring capability, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent leverages the multi-functionality of millimeter-wave transceivers that are already integrated into 5G devices and access points. These existing devices serve both communication purposes and sleep monitoring functions, eliminating the need for separate dedicated monitoring hardware and reducing overall system complexity while maintaining monitoring precision.
Solution Approach 2:
The patent enables existing 5G wireless devices to serve dual purposes: maintaining their primary communication function while simultaneously performing sleep disruption monitoring. The millimeter-wave transceivers in these devices automatically capture reflected signals for analysis without requiring additional specialized hardware, making the system self-sufficient and complexity-free.
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
This approach provides non-invasive, privacy-friendly, and accurate monitoring of sleep disruptions, improving detection accuracy and reducing false alarms, while ensuring the comfort and safety of subjects.
Implementation Method 1
receiving millimeter-wave (mmWave) wireless signals reflecting from the human subject
Data Source
AI summary
Methodology and corresponding apparatus pertains to sleep disruption monitoring, including use of a wireless signal-based monitoring system leveraging millimeter-wave technology. A software-only sleep disruption monitoring solution can be based on millimeter-wave (mmWave) wireless-based solutions which leverage cross-correlation between successive mmWave reflected signals and a Hidden Markov Model (HMM) to identify respective sleep (rest) and disruptions (toss-turn) periods. A toss-turn detector module can identify sudden movements during sleep from mmWave wireless signals and classify the sleeping period into the two states: Rest or toss-turn. Whenever mmWave transceivers (such as included in 5G-and-beyond devices) are implemented as access points, in mass privacy non-invasive sleep disruption monitoring can be provided for consumers at home.