Anti-snoring Device Control via Dynamic Sound Analysis
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Solution Overview
Problem
Current anti-snoring devices rely on prerecorded empirical values to determine snoring states, which can be inaccurate and fail to effectively stop snoring, especially in deep sleep conditions.
Innovation Solution
The method involves capturing sound data, sampling it continuously, extracting periodic sound characteristic sections, and stopping snoring when their repeated occurrence count reaches a threshold, using a combination of filtering and intensity sequencing to differentiate between snoring and background noise, and employing vibration or sound signals to disrupt snoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If prerecorded empirical values are used to determine snoring states, then the control method is simple, but the accuracy of snoring detection is poor
Solution Approach 1:
The patent transitions from static empirical threshold values to dynamic real-time sound analysis. The system continuously captures sound data, performs short-time Fourier transform to obtain spectral features, and dynamically adjusts detection thresholds based on actual sleep conditions, thereby improving detection accuracy while maintaining operational simplicity through automated processing.
Solution Approach 2:
The patent replaces the mechanical comparison method (matching against prerecorded empirical values) with signal processing techniques. By using short-time Fourier transform to extract spectral features and comparing these features against dynamically determined thresholds, the system achieves higher accuracy without significantly increasing operational complexity.
2Measurement precision
If continuous sound monitoring is performed to accurately detect snoring, then the detection accuracy improves, but the energy consumption increases
Solution Approach 1:
The patent implements periodic sound monitoring rather than continuous monitoring. It divides the monitoring period into multiple time windows, performs Fourier transform analysis at intervals, and uses threshold-based detection to determine when full analysis is necessary. This periodic approach maintains detection accuracy while significantly reducing computational load and energy consumption during low-activity periods.
Solution Approach 2:
The system performs partial analysis continuously (basic sound level monitoring) and conducts full spectral analysis only when necessary (when snoring is detected or suspected). This selective approach ensures accurate detection when needed while minimizing energy consumption during normal sleep periods through reduced processing intensity.
3Device complexity
If empirical threshold values are used for snoring detection, then the device complexity is low, but the reliability of anti-snoring function is poor
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors sound characteristics, compares them against dynamically adjusted thresholds, and adapts its detection parameters based on detected patterns. The system provides feedback to users about detection results and adjusts its sensitivity based on false positive/negative rates, thereby improving reliability without requiring overly complex algorithms.
Solution Approach 2:
The system performs preliminary sound quality assessment and preprocessing (noise filtering, signal normalization) before main detection analysis. By preparing the data in advance and removing obvious non-snoring sounds through preliminary filtering, the main detection algorithm can operate more reliably with simpler logic, as the preprocessing step eliminates many false detection scenarios.
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 accurately identifies snoring and effectively stops it by using threshold-based analysis and adaptive signal intensities, improving snoring cessation even during deep sleep.
Implementation Method 1
employing vibration or sound signals to disrupt snoring
Implementation Method 2
capturing sound data within a period of time
Data Source
AI summary
A method of controlling an anti-snoring device is disclosed. The method includes the steps of capturing sound data within a period of time, sampling the sound data continuously, extracting multiple sound characteristic sections arising periodically from the sound data, and activating the anti-snoring device to stop snoring when the repeated occurrence count of the sound characteristic sections reach a threshold value.