Cloud-Based Sleep Stage Detection for Adaptive Bed Comfort Control

The patent introduces a remote server as an intermediary between the bed system and the sleep stage detection process. The server hosts the machine learning classifiers and receives pressure data from the bed, processes it remotely, and returns detection results. This mediator approach allows the bed system to achieve high measurement precision through sophisticated AI algorithms without requiring complex local computing infrastructure, thus resolving the contradiction between detection accuracy and device complexity.

The patent replaces traditional mechanical or rule-based sleep detection methods with machine learning-based classification systems. Instead of using simple threshold-based algorithms or mechanical sensors, the system employs trained neural networks and support vector machines that automatically learn complex patterns from pressure data. This substitution enables highly accurate sleep stage detection while the computational complexity is handled by the remote server rather than embedded in the bed's hardware architecture.

The patent implements preliminary action by pre-training the machine learning classifiers on extensive datasets before deployment. The remote server maintains trained models that have already learned to accurately classify sleep stages from pressure patterns. When actual sleep data arrives, the pre-trained classifiers can process it rapidly without requiring time-consuming training computations, thus minimizing data processing time while maintaining high measurement precision.

The patent uses copying by transmitting only the essential pressure data parameters to the remote server rather than raw continuous sensor streams. The system extracts and sends key features and aggregated metrics that capture the essential sleep patterns, reducing the volume of data that needs to be transmitted and processed. This copying approach maintains detection accuracy while significantly reducing communication and processing time.