Pulmonary Monitoring Using Smartphones

Larson, Eric C.; Saba, Elliot; Kaiser, Spencer A.; Goel, Mayank; Patel, Shwetak

doi:10.1007/978-3-319-51394-2_13

Cited by 7 publications

(5 citation statements)

References 64 publications

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“…The mouthpiece was attached to the iPhone microphone using a custom-made 3D-printed adapter (see Figure 2 a,b). To achieve the spirometry measurement, much effort has been carried out on the vortex whistle [ 30 , 31 , 32 , 33 ]. We modified the vortex whistle available for the smartphone built-in microphone.…”

Section: Methodsmentioning

confidence: 99%

High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

Thap

Chung

Jeong

et al. 2016

Sensors

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In this paper, a smartphone-based lung function test, developed to estimate lung function parameters using a high-resolution time-frequency spectrum from a smartphone built-in microphone is presented. A method of estimation of the forced expiratory volume in 1 s divided by forced vital capacity (FEV1/FVC) based on the variable frequency complex demodulation method (VFCDM) is first proposed. We evaluated our proposed method on 26 subjects, including 13 healthy subjects and 13 chronic obstructive pulmonary disease (COPD) patients, by comparing with the parameters clinically obtained from pulmonary function tests (PFTs). For the healthy subjects, we found that an absolute error (AE) and a root mean squared error (RMSE) of the FEV1/FVC ratio were 4.49% ± 3.38% and 5.54%, respectively. For the COPD patients, we found that AE and RMSE from COPD patients were 10.30% ± 10.59% and 14.48%, respectively. For both groups, we compared the results using the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and found that VFCDM was superior to CWT and STFT. Further, to estimate other parameters, including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and peak expiratory flow (PEF), regression analysis was conducted to establish a linear transformation. However, the parameters FVC, FEV1, and PEF had correlation factor r values of 0.323, 0.275, and −0.257, respectively, while FEV1/FVC had an r value of 0.814. The results obtained suggest that only the FEV1/FVC ratio can be accurately estimated from a smartphone built-in microphone. The other parameters, including FVC, FEV1, and PEF, were subjective and dependent on the subject’s familiarization with the test and performance of forced exhalation toward the microphone.

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Section: Methodsmentioning

confidence: 99%

High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

Thap

Chung

Jeong

et al. 2016

Sensors

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“…[13] The wireless electronic devices are initially projected to ease the hassle initiated by determining portable widgets to evaluate symptoms. [14], [15] Fingertip oximeter is one of the examples as it was replaced by smartphones that can monitor oxygen saturation. [16] The major advantage of wireless sensing is constant observation without any contact with the body.…”

Section: Introductionmentioning

confidence: 99%

UWB Radar Sensing for Respiratory Monitoring Exploiting Time- Frequency Spectrograms

Badshah

Saeed

Momand

et al. 2022

2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH)

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“…Though there is no standard for the management and control of respiratory disorders, each ailment has an acceptable measure of wellness [8,9]. Many researchers have focused on the use of advanced information and communications technologies to improve overall management and control of respiratory health conditions [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…However, lung or breath sound detection is not sufficient to provide absolute functional solution to pulmonary exacerbation. There is need to measure and analyze other vital signs including environmental effects as well as patient's level of activity, to provide accurate and reliable information in controlling the respiratory conditions [8,13].…”

Section: Introductionmentioning

confidence: 99%

Certainty Modeling of a Decision Support System for Mobile Monitoring of Exercise-induced Respiratory Conditions

Uwaoma

Mansingh

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Mobile health systems in recent times, have notably improved the healthcare sector by empowering patients to actively participate in their health, and by facilitating access to healthcare professionals. Effective operation of these mobile systems nonetheless, requires high level of intelligence and expertise implemented in the form of decision support systems (DSS). However, common challenges in the implementation include generalization and reliability, due to the dynamics and incompleteness of information presented to the inference models. In this paper, we advance the use of ad hoc mobile decision support system to monitor and detect triggers and early symptoms of respiratory distress provoked by strenuous physical exertion. The focus is on the application of certainty theory to model inexact reasoning by the mobile monitoring system. The aim is to develop a mobile tool to assist patients in managing their conditions, and to provide objective clinical data to aid physicians in the screening, diagnosis, and treatment of the respiratory ailments. We present the proposed model architecture and then describe an application scenario in a clinical setting. We also show implementation of an aspect of the system that enables patients in the self-management of their conditions.

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Pulmonary Monitoring Using Smartphones

Cited by 7 publications

References 64 publications

High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

UWB Radar Sensing for Respiratory Monitoring Exploiting Time- Frequency Spectrograms

Certainty Modeling of a Decision Support System for Mobile Monitoring of Exercise-induced Respiratory Conditions

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