Sleep quality improvement method, electronic device, and program product

By integrating multimodal data and generating personalized intervention actions, the accuracy and personalization issues of home sleep monitoring devices have been resolved, a closed-loop system has been constructed, and efficient sleep quality improvement has been achieved.

CN122177364APending Publication Date: 2026-06-09XIAMEN LEELEN TECH CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
XIAMEN LEELEN TECH CO LTD
Filing Date
2026-01-29
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing home sleep monitoring devices suffer from problems such as insufficient accuracy of single-modal data, simplistic multi-source data fusion methods, lack of personalized user modeling, separation of monitoring and intervention, lack of personalized intervention plans, inaccurate timing of intervention, and lack of closed-loop feedback mechanisms, resulting in unsatisfactory sleep quality improvement effects.

Method used

By collecting and fusing multimodal time-series data, and combining user profiles and device status, personalized final intervention actions are generated, constructing a closed loop of 'monitoring-intervention-feedback-optimization' to achieve high-precision sleep state recognition and dynamic intervention, and optimizing strategy models to improve sleep improvement rates.

Benefits of technology

It achieves high-precision sleep state recognition and personalized intervention, improves intervention matching and user experience, builds a continuously evolving closed-loop system, dynamically adjusts intervention strategies, and optimizes the effect of improving sleep quality.

✦ Generated by Eureka AI based on patent content.

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Abstract

This disclosure provides a method, electronic device, and program product for improving sleep quality, relating to the field of sleep management. The sleep quality improvement method includes: collecting multimodal data at various times from the start of sleep to the first moment, and forming multimodal time-series data; fusing the multimodal time-series data to obtain fused feature time-series data; determining the sleep state at the first moment; generating a final intervention action through a strategy optimization model based on the sleep state at the first moment, the multimodal data at the first moment, a user profile, and the device state at the first moment, where the user profile represents the user's sleep-related characteristics; controlling the device corresponding to the final intervention action to execute the final intervention action; repeating the above steps until the sleep process ends; determining the sleep improvement rate based on the multimodal data and sleep state at various times during the sleep process; and optimizing the strategy optimization model based on the sleep improvement rate.
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