Wearable Patch for Breathing Pattern Recognition
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Solution Overview
Problem
Current wearable devices for monitoring breathing patterns are limited by high costs, complexity, and inaccuracies due to movement artifacts, making it difficult to detect breathing rates and volumes over extended periods without sophisticated equipment and trained personnel.
Innovation Solution
A wearable patch system using a combination of an accelerometer and a capacitive pressure sensor, filtered with a type II Chebyshev low-pass filter and processed with Hilbert transforms, to accurately measure breathing patterns by distinguishing between breathing-related and non-breathing-related body movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single sensor is used for breathing monitoring, then the device complexity is reduced, but measurement precision deteriorates due to inability to distinguish breathing-related from non-breathing-related movements
Solution Approach 1:
The patent combines multiple sensors (accelerometer, pressure sensor, temperature sensor, humidity sensor) into an integrated wearable monitoring device. This merging allows the system to capture multiple physiological parameters simultaneously and use sensor fusion algorithms to distinguish breathing-related movements from other body movements, thereby improving measurement precision while maintaining manageable device complexity through integrated design.
2Measurement precision
If complex signal processing algorithms are applied, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the signal processing into distinct stages: raw data acquisition from multiple sensors, preprocessing (filtering, noise reduction), feature extraction (breathing rate, tidal volume calculation), and clinical interpretation. This segmentation allows complex algorithms to be applied systematically at each stage, improving measurement precision while managing overall system complexity through modular processing architecture.
Solution Approach 2:
The patent introduces intermediate processing layers between raw sensor data and final breathing metrics. Signal processing algorithms act as intermediaries that transform raw accelerometer and pressure sensor data into meaningful breathing patterns, filtering out non-breathing-related movements and extracting clinically relevant information without requiring the end system to handle all complexity directly.
3Productivity
If wearable sensors are used for continuous monitoring, then productivity improves, but reliability deteriorates due to movement artifacts and false readings
Solution Approach 1:
The patent implements a multi-functional sensor system that can detect multiple types of movements and physiological signals (breathing, heart rate, body temperature, humidity). This universality allows the device to continuously monitor breathing while simultaneously identifying and compensating for non-breathing-related movements, maintaining reliability during various activity states including sleep, rest, and light exercise.
Solution Approach 2:
The patent employs feedback mechanisms where sensor data is continuously analyzed and used to adjust monitoring parameters in real-time. The system provides feedback on detected movement patterns and adjusts signal processing accordingly, allowing continuous productivity while maintaining reliability by correcting for movement artifacts as they occur rather than relying on static calibration.
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
The system provides reliable and accurate long-term monitoring of breathing patterns, improving detection of respiratory events and tidal volumes, reducing false readings and enhancing clinical-grade data acquisition.
Implementation Method 1
an accelerometer for measuring a breathing vibration
Implementation Method 2
a pressure sensor for measuring a breathing pressure
Data Source
AI summary
In a preferred embodiment, there is provided a method for determining a breathing pattern, the method comprising: placing a motion sensor and a flex sensor proximate to a diaphragm of a subject; obtaining time domain signals or data from both said sensors over a time period; subjecting the time domain signals or data from the motion sensor to Fast Fourier transformation to obtain frequency domain signals or data, and determining from the frequency domain signals or data one or more frequencies associated with body motion of the subject substantially unrelated to breathing; and filtering the time domain signals or data from the flex sensor to remove information related to the body motion therefrom to obtain filtered time domain signals or data.