Sleep State Classification From BCG Signals Without Wearables
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Solution Overview
Problem
Existing sleep monitoring technologies often require invasive contact-based sensors, which can interfere with sleep comfort and are less accurate due to processing power limitations, making real-time sleep state detection challenging.
Innovation Solution
Utilizing machine learning models trained on ballistocardiography (BCG) signals to detect sleep states non-invasively and in real-time, employing convolutional neural networks (CNN) and recurrent neural networks (RNN) to classify sleep stages without requiring wearable devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contact-based sensors are used for sleep monitoring, then measurement precision can be improved, but device complexity and ease of operation worsen due to invasive nature and processing power limitations
Solution Approach 1:
The patent replaces mechanical contact-based sensors with a bed system that detects sleep states through ballistocardiography signals generated by the user's body movements and cardiac activity on the bed surface. This substitution eliminates the need for invasive wearable devices while maintaining detection capability through the bed's structural sensing elements.
Solution Approach 2:
The patent introduces ballistocardiography signals as an intermediary measurement medium. Instead of directly contacting the user with sensors, the system measures the mechanical effects of the user's physiological activity on the bed structure, using these intermediate signals to infer sleep states without direct sensor contact.
2Measurement precision
If contact-based sensors are used for sleep monitoring, then measurement precision can be improved, but ease of operation worsens due to user comfort interference
Solution Approach 1:
The patent replaces uncomfortable wearable sensors with a bed-based detection system that uses the bed structure itself to sense physiological signals. This eliminates the need for users to wear any devices, completely removing the comfort interference issue while maintaining measurement capability.
3Productivity
If machine learning models with CNN and RNN are used for sleep state classification, then productivity and measurement precision improve, but use of energy and device complexity increase
Solution Approach 1:
The patent segments the sleep state detection process into distinct stages: signal acquisition from the bed, feature extraction from ballistocardiography signals, and classification using machine learning models. This segmentation allows each component to be optimized independently, reducing overall computational energy requirements while maintaining real-time detection capability.
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
Enables accurate, real-time sleep state detection with minimal computational resources, allowing for timely environmental adjustments to enhance sleep quality without disturbing the user.
Implementation Method 1
One or more machine learning models can be trained and used to determine sleep staging of the user based on ballistocardiography (BCG) signals from that user
Data Source
AI summary
Cardiac data defining at least inter-beat interval (IBI) sequences is received. Tagging data that defines tags of sleep-states for the IBI sequences is received. A sleep-state classifier is generated using the cardiac data and the tagging data, the generating may include: extracting the IBI sequences from the cardiac data; training a convolutional neural network (CNN) using as input the cardiac data and the tagging data to generate intermediate data; and iteratively training a recurrent neural network (RNN) configured to produce state data as output, the iterative training of the RNN using i) the intermediate data as an initial input and ii) the intermediate data and a previous state data as subsequent input.