Bed system including pressure sensor
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Solution Overview
Problem
Existing bed systems struggle to accurately determine biometric parameters for users with cardiac conditions such as atrial fibrillation due to noise interference in ballistocardiogram signals, which affects the controller's ability to manage sleep environment adjustments.
Innovation Solution
A bed system with pressure sensors that collect ballistocardiogram signals, uses a machine learning model to generate secondary biometric parameters, and trains a bed actuation control model to account for cardiac conditions, enabling precise control of actuation devices based on user biometrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the controller uses ballistocardiogram signals to determine biometric parameters, then the sleep environment can be automatically adjusted, but the measurement accuracy deteriorates for users with cardiac conditions like atrial fibrillation due to noise interference
Solution Approach 1:
The patent introduces an intermediary processing layer between the pressure sensor and the controller. This layer includes signal preprocessing modules that filter and clean the ballistocardiogram signals before they reach the biometric parameter determination algorithm, thereby maintaining automation while improving measurement accuracy for users with cardiac conditions
Solution Approach 2:
The system dynamically adjusts signal processing parameters based on detected cardiac conditions. When atrial fibrillation is detected, the system changes filtering parameters, sampling rates, and analysis windows to optimize biometric parameter extraction from noisy signals, resolving the contradiction between automation and measurement precision
2Measurement precision
If the system collects and processes additional biometric signal data to account for cardiac conditions, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system performs preliminary characterization of the user's cardiac condition during an initial monitoring period. This preliminary action creates a personalized profile that simplifies subsequent signal processing by pre-configuring appropriate filtering and analysis parameters, thereby improving measurement precision without proportionally increasing device complexity during ongoing use
Solution Approach 2:
The system automatically detects cardiac conditions and self-adjusts its signal processing parameters without requiring manual intervention or complex external configuration. The algorithm autonomously identifies atrial fibrillation patterns and adapts processing accordingly, improving measurement accuracy while keeping the user interface and operational complexity low
Data Source
AI summary
A bed includes one or more actuation devices and a pressure sensor configured to generate a pressure signal. The bed system also includes control circuitry comprising one or more memories configured to store a machine learning model and a bed actuation control model; and processing circuitry in communication with the one or more memories. The processing circuitry is configured to receive a first biometric signal indicating a first biometric parameter over a period of time; apply, based on the first biometric signal, the machine learning model to generate a second biometric signal, wherein the second biometric signal indicates a second biometric parameter over the period of time, wherein the pressure signal indicates a user sample of the second biometric parameter corresponding to a user laying on the bed; and train, using the second biometric signal, the bed actuation control model to control the one or more actuation devices.