Respiratory State Estimation Using Trunk Movement Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current simple tests for sleep apnea syndrome cannot distinguish between obstructive and central sleep apnea, placing a burden on patients and hospitals, and requiring more invasive polysomnography tests for accurate diagnosis.
Innovation Solution
A lightweight, portable respiratory state estimation device with a respiratory measurement module, body movement measurement module, respiration detector, body movement detector, and output module that uses sensors to detect respiratory fluctuations and trunk movement during sleep to estimate respiratory states, providing information to differentiate between obstructive and central sleep apnea.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography test is conducted to accurately diagnose sleep apnea syndrome, then measurement precision is improved, but device complexity and burden on patient increase
Solution Approach 1:
The patent extracts only the essential measurement functions needed for sleep apnea diagnosis from the complex polysomnography system. Specifically, it uses a simple accelerometer to detect body movement during sleep, which is the key indicator for distinguishing obstructive from central sleep apnea. By taking out only the necessary movement detection function, the system achieves accurate diagnosis without requiring the complex full polysomnography setup.
Solution Approach 2:
The patent employs a simple, inexpensive accelerometer that can be easily attached to the patient's body during sleep. This disposable-like simple sensor replaces the expensive, complex polysomnography equipment. The accelerometer is sufficient for the specific task of detecting body movement patterns, providing cost-effective and simple sleep apnea screening without requiring hospital infrastructure.
2Ease of operation
If simple test with basic sensor is used to reduce burden on patient, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces complex mechanical sensing systems (polysomnography with multiple sensors including nasal respiration sensors, abdominal bands) with a simple accelerometer-based mechanical movement detection system. The accelerometer detects body movement patterns that indirectly indicate respiratory status, achieving sufficient precision for distinguishing sleep apnea types without the complexity of direct respiratory measurement hardware.
Solution Approach 2:
The patent introduces body movement as an intermediary indicator to indirectly assess respiratory state. Instead of directly measuring respiration (which requires invasive or complex sensors), the system uses body movement detection as a mediator that correlates with respiratory patterns. This intermediary approach enables simple, non-invasive measurement while maintaining diagnostic accuracy for sleep apnea discrimination.
3Device complexity
If basic respiratory measurement is performed to simplify device, then device complexity is reduced, but loss of information increases
Solution Approach 1:
The patent changes the measurement parameter from direct respiratory flow or oxygen saturation to body movement acceleration. By detecting changes in body movement patterns (acceleration magnitude and frequency) during sleep, the system captures distinctive signatures of obstructive versus central sleep apnea. This parameter transformation enables simple device implementation while preserving critical diagnostic information about sleep apnea type.
Data Source
AI summary
According to one embodiment, a respiratory measurement module measures a first signal related to a respiratory fluctuation of the user. A body movement measurement module measures a second signal related to a movement of a trunk of the user. A respiration detector detects whether or not the user is in a respiratory arrest state, based on the measured first signal measured. A body movement detector detects whether or not the trunk of the user is moving, based on the measured second signal. An estimation module estimates a respiratory state of the user, based on a detection result by the respiration detector and a detection result by the body movement detector. An output module outputs respiratory state information indicating the estimated respiratory state.


