Smartphone mmWave Spirometry for Noise-Resistant Home Lung Testing
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Solution Overview
Problem
Existing at-home spirometry systems are costly, require extra hardware, or provide limited information, and are susceptible to noise and motion, making them unreliable for continuous lung function monitoring.
Innovation Solution
An integrated system using 5G smart devices with built-in millimeter-wave (mmWave) technology and a software-only spirometry solution, leveraging deep learning and CNN-LSTM to analyze airflow-induced vibrations for contactless lung function monitoring, providing key spirometry indicators without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If audio signals are used for lung function monitoring, then cost is reduced, but reliability deteriorates due to susceptibility to noise and motion
Solution Approach 1:
The patent replaces acoustic sensing (microphone-based audio signals) with millimeter-wave radar sensing. This substitution uses electromagnetic wave reflection and phase detection to measure respiratory motion, eliminating susceptibility to acoustic noise and motion artifacts while maintaining low cost through integration in 5G smartphones.
Solution Approach 2:
The patent changes the sensing parameter from acoustic frequency to millimeter-wave frequency. By detecting phase changes in reflected mmWave signals caused by respiratory motion, the system achieves motion-resistant lung function monitoring that is insensitive to environmental noise and body movements.
2Ease of operation
If contactless lung function monitoring systems are developed, then ease of operation is improved, but device complexity increases due to additional hardware requirements
Solution Approach 1:
The patent leverages the millimeter-wave radar transceiver already integrated in 5G smartphones for communication purposes, making it serve a dual function for lung function monitoring. This eliminates the need for separate sensing hardware, reducing device complexity while maintaining contactless operation.
Solution Approach 2:
The system uses the smartphone's own built-in mmWave transceiver to perform both communication and health sensing functions. The device serves itself by utilizing its existing hardware capabilities without requiring external sensors or additional components.
3Measurement precision
If existing spirometers are used for at-home monitoring, then measurement precision is improved, but device complexity increases due to extra hardware requirements
Solution Approach 1:
The patent replaces mechanical spirometry systems with electromagnetic wave-based phase detection. By measuring phase changes in reflected mmWave signals caused by respiratory-induced vibrations, the system achieves clinical-grade measurement precision using only the smartphone's existing radar hardware.
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 offers a low-cost, reliable, and motion-resistant method for at-home spirometry, accurately predicting lung function indicators comparable to clinical spirometers with less than 5% prediction error.
Implementation Method 1
Airflow on the device surface creates tiny vibrations which directly affect the phase of the reflected mmWave signal from nearby objects
Data Source
AI summary
An integrated system and associated methodology allow performing at-home spirometry tests using smart devices which leverage the built-in millimeter-wave (mmWave) technology. Implementations leverage deep learning with some embodiments including a combination of mmWave signal processing and CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory Network) architecture. Smartphone devices are transformed into reliable at-home spirometers by having a user hold a device in front of their mouth, inhale their full lung volume, and forcibly exhale until the entire volume is expelled, as in typical spirometry tests. Airflow on the device surface creates tiny vibrations which directly affect the phase of the reflected mmWave signal from nearby objects. Stronger airflow yields larger vibration and higher phase change. The technology analyzes tiny vibrations created by airflow on the device surface and combines wireless signal processing with deep learning. The resulting low-cost, contactless method of lung function monitoring is not affected by noise and motion and provides all key spirometry indicators.