Progressive acoustic therapy scene generation method and system for sleep disorder intervention

By analyzing multimodal sleep monitoring data and gradually adjusting the sound scene, the problem of insufficient dynamic response in existing sleep intervention systems has been solved, achieving precise intervention in sleep state and improved stability throughout the night.

CN122333368APending Publication Date: 2026-07-03SOUND MEDICINE VALLEY (GUANGZHOU) HEALTH MANAGEMENT CO LTD
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Patent Information

Application Number
CN202610532490.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-21
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing sleep intervention systems lack a mechanism for continuous tracking and dynamic response to the evolution of sleep states. The adjustment of sound therapy parameters is simple and direct, and cannot provide flexible, graded, and adaptive intervention before micro-awakening occurs.

Method used

By acquiring multimodal sleep monitoring data, characteristic parameters of sleep stability and micro-arousal precursors are extracted. The risk value of micro-arousal is determined by weighted fusion analysis using short-term analysis window and trend analysis window. Combined with the entry/exit threshold mechanism, the sound scene is gradually adjusted to achieve flexible and graded sound therapy intervention.

Benefits of technology

It effectively improves the continuity and stability of sleep throughout the night, avoids secondary disturbances caused by sudden changes in sound, and continuously improves the accuracy of prediction and intervention through individualized learning.

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Abstract

The invention relates to the technical field of sleep monitoring, in particular to a progressive acoustic therapy scene generation method and system oriented to sleep disorder intervention. The method comprises the steps that multi-mode sleep monitoring data in the sleep process of a user is acquired and preprocessed, and sleep stability characteristic parameters and micro-awakening precursor characteristic parameters are extracted; carrying out weighted fusion analysis on the basis, and determining a risk value of a micro-awakening event in a future prediction window; based on this, determining a risk level and matching a corresponding scene control parameter; judging whether to switch the risk level based on the entry threshold and the exit threshold; after entering the risk level is confirmed, carrying out progressive adjustment on the current sound scene to generate a target sound scene; after the prediction window is finished, the risk judgment threshold value, the feature weight and the scene control parameter set are updated according to the actual sleep state. According to the method, the prediction and intervention precision is continuously improved through individualized learning, so that the continuity and stability of all-night sleep are effectively improved.
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