Wireless respiratory monitoring and coughing detection using a wearable patch sensor network

Elfaramawy, Tamer; Fall, Cheikh Latyr; Morissette, Martin; Lellouche, François; Gosselin, Benoît

doi:10.1109/newcas.2017.8010139

Cited by 27 publications

(17 citation statements)

References 10 publications

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“…Point-of-care or semicontinuous methods for quantifying coughing or other vocal activities rely on electromyography, respiratory inductive plethysmography, accelerometry, or auditory recordings captured with one or several sensors, sometimes with other exploratory approaches (e.g., the nasal thermistor or the electrocardiography) (36)(37)(38)(39)(40)(41). Digital signal processing followed by machine learning algorithms often serves as the basis for classification (42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53).…”

Section: Significancementioning

confidence: 99%

Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients

Ouyang²,

Jeong³

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups.

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Section: Significancementioning

confidence: 99%

Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients

Ouyang²,

Jeong³

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…This system uses a high definition camera. Elfaramawy et al (2017) discuss a system that has both contact-based and noncontact mechanisms. The wireless system monitors respiration while a wearable system detects a patient coughing.…”

Section: Heart and Blood Related Diseases Monitoring Systemsmentioning

confidence: 99%

Remote patient monitoring: a comprehensive study

Malasinghe

Ramzan

Dahal

2017

J Ambient Intell Human Comput

384

180

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“…For this reason, literature offers several solutions for the coughing detection and classification. These solutions are usually based on the using of audio microphones [61], [62].…”

Section: E Pulmonary Diseases Sensorsmentioning

confidence: 99%

E-health-IoT Universe: A Review

Scarpato¹,

Pieroni²,

Nunzio³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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The Internet of Things (IoT) devices are able to collect and share data directly with other devices through the cloud environment, providing a huge amount of information to be gathered, stored and analyzed for data-analytics processes. The scenarios in which the IoT devices may be useful are amazing varying, from automotive, to industrial automation or remote monitoring of domestic environment. Furthermore, has been proved that healthcare applications represent an important field of interest for IoT devices, due to the capability of improving the access to care, reducing the cost of healthcare and most importantly increasing the quality of life of the patients. In this paper, we analyze the state-of-art of IoT in medical environment, illustrating an extended range of IoT-driven healthcare applications that, however, still need innovative and high technology-based solutions to be considered ready to market. In particular, problems regarding characteristics of response-time and precision will be examined. Furthermore, wearable and energy saving properties will be investigated in this paper and also the IT architectures able to ensure security and privacy during the all data-transmission process. Finally, considerations about data mining applications, such as risks prediction, classification and clustering will be provided, that are considered fundamental issues to ensure the accuracy of the care processes.

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Wireless respiratory monitoring and coughing detection using a wearable patch sensor network

Cited by 27 publications

References 10 publications

Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients

Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients

Remote patient monitoring: a comprehensive study

E-health-IoT Universe: A Review

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