Breathe Analysis For Identification Of Respiratory Abnormalities Using SVM

Bernard, Felcya Grace; Mounikaa., S; Nandhini, R.; Mohanarathinam, A.

doi:10.1109/icaccs48705.2020.9074223

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…In this work, we target continuous non-intrusive user verification for the IoT-based environment. Towards this end, we find that breathing rate, which is a major indicator of a person's vital signs, can serve as a good biometric feature to facilitate continuous non-intrusive user verification [8], [9]. Specifically, the breath of an individual always maintains a steady rate (i.e., 12-20 bpm) and chest motion pattern (i.e., amplitude and variation trend), while different people induce different motion patterns of chest.…”

Section: Introductionmentioning

confidence: 99%

Breathing-based continuous non-intrusive user verification leveraging commodity WiFi

Dai

Jiang

et al. 2022

J. Commun. Netw.

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This paper opens up a new pathway of the utility of breathing pattern for user verification. We demonstrate that it is possible to capture people's breathing pattern leveraging commodity WiFi devices. While prior solutions for biometricsbased user recognition usually require dedicated devices (e.g., video cameras or IR sensors), this paper introduces the first general, low-cost breathing-based user verification system using commodity WiFi devices. The proposed system is based on the fact that the breathing pattern always keeps consistent for the same user but distinct among different people. Our innovative method successfully extracts the breathing pattern of different people based on channel state information of WiFi signal to facilitate user verification. The prototype study using two commodity WiFi devices can differentiate people with an average verification accuracy over 90%, suggesting that our breathing-based user verification system using commerical offthe-shelf (COTS) WiFi is promising to be one of the most critical methods in biometrics.

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Section: Introductionmentioning

confidence: 99%

Breathing-based continuous non-intrusive user verification leveraging commodity WiFi

Dai

Jiang

et al. 2022

J. Commun. Netw.

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Comparing the Selectivity of Solid Phase Microextraction Fibers to Detect Volatile Organic Compounds

Woollam

Grocki

Rivera

et al. 2021

2021 IEEE International Conference on Design &Amp; Test of Integrated Micro &Amp; Nano-Systems (DTS)

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TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm

Rajeshkumar,

Soundar

2024

THC

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BACKGROUND: Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnormalities. OBJECTIVE: Existing methods for lung abnormality detection often rely on invasive and time-consuming procedures limiting their effectiveness in real-time diagnosis. This work introduces a novel Touchless Lung Abnormality (TO-LAB) detection model utilizing universal software radio peripherals (USRP) and machine learning algorithms. METHODS: The TO-LAB model integrates a blood pressure meter and an RGB-D depth-sensing camera to gather individual data without physical contact. Heart rate (HR) is analyzed through image conversion to IPPG signals, while blood pressure (BP) is obtained via analog conversion from the blood pressure meter. This touchless imaging setup facilitates the extraction of essential signal features crucial for respiratory pattern analysis. Advanced computer vision algorithms like Mel-frequency cepstral coefficients (MFCC) and Principal Component Analysis (PCA) process the acquired data to focus on breathing abnormalities. These features are then combined and inputted into a machine learning-based Multi-class SVM for breathing activity analysis. The Multi-class SVM categorizes breathing abnormalities as normal, shallow, or elevated based on the fused features. The efficiency of this TO-LAB model is evaluated with the simulated and real-time data. RESULTS: According to the findings, the proposed TO-LAB model attains the maximum accuracy of 96.15% for real time data; however, the accuracy increases to 99.54% for simulated data for the efficient classification of breathing abnormalities. CONCLUSION: From this analysis, our model attains better results in simulated data but it declines the accuracy while processing with real-time data. Moreover, this work has a significant medical impact since it presents a solution to the problem of gathering enough data during the epidemic to create a realistic model with a large dataset.

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Breathe Analysis For Identification Of Respiratory Abnormalities Using SVM

Cited by 3 publications

References 10 publications

Breathing-based continuous non-intrusive user verification leveraging commodity WiFi

Breathing-based continuous non-intrusive user verification leveraging commodity WiFi

Comparing the Selectivity of Solid Phase Microextraction Fibers to Detect Volatile Organic Compounds

TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm

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