Full-stack sleep optimization method and system based on foot bottom and upper limb motion perception, medium
By combining smart insoles and wrist-worn sensors to collect multimodal data, perform time synchronization and dynamic calibration, and use machine learning models to predict changes in physiological indicators during the circadian rhythm and sleep cycle, the system solves the problems of insufficient quantification of physiological load and discontinuous data transfer in existing systems, and achieves high-precision sleep optimization and closed-loop regulation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-21
- Publication Date
- 2026-05-29
AI Technical Summary
Existing sleep optimization systems lack the ability to capture the continuity and causal relationships of human physiological states. They rely on low-quality input data, resulting in insufficient accuracy in quantifying physiological load and an inability to achieve precise sleep recovery models. Furthermore, the discontinuous transition between daytime and nighttime data affects the predictive regulation effect of the system.
By combining smart insoles and wrist-worn sensors to collect multimodal data, time synchronization and dynamic calibration are performed. Machine learning models are used to predict changes in physiological indicators during the circadian rhythm and sleep cycle, generating personalized intervention suggestions and achieving closed-loop optimization.
It achieves high-precision physiological load input and closed-loop predictive regulation of the diurnal causal chain, improves the accuracy of sleep optimization and the logical continuity of the system, generates targeted and compensatory environmental intervention suggestions, and enhances sleep recovery.
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Figure CN122097790A_ABST
Abstract
Citation Information
Patent Citations
Fatigue monitoring and management system
EP3054836A1
Systems and methods for optimization of sleep and post-sleep performance
WO2010042615A3