Exploration of Cough Recognition Technologies Grounded on Sensors and Artificial Intelligence

Preethi, S.; Revathi, A.; Murugan, M.

doi:10.1007/978-981-15-8097-0_8

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…For detection of cough, there are a few approaches that utilize sensors for its detection [52,54,55]. Our supported sensor, the SW-420 is Doppler radar for cough detection.…”

Section: The Hardware Componentsmentioning

confidence: 99%

“…Similarly, the authors of [51] developed an application that gathers self-reported symptoms as well as smartwatch and activity tracker data in order to differentiate between COVID-19 negative and positive cases in symptomatic persons. • Use of Cough Recognition Techniques: Cough [52] is a characteristic of varied respiratory infections from a common cold to the latest coronavirus infection. Not only does cough exist in humans, but it has been equally found to exist in many species [53].…”

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confidence: 99%

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An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors

Mukhtar

Rubaiee

Krichen

et al. 2021

IJERPH

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Experts have predicted that COVID-19 may prevail for many months or even years before it can be completely eliminated. A major problem in its cure is its early screening and detection, which will decide on its treatment. Due to the fast contactless spreading of the virus, its screening is unusually difficult. Moreover, the results of COVID-19 tests may take up to 48 h. That is enough time for the virus to worsen the health of the affected person. The health community needs effective means for identification of the virus in the shortest possible time. In this study, we invent a medical device utilized consisting of composable sensors to monitor remotely and in real-time the health status of those who have symptoms of the coronavirus or those infected with it. The device comprises wearable medical sensors integrated using the Arduino hardware interfacing and a smartphone application. An IoT framework is deployed at the backend through which various devices can communicate in real-time. The medical device is applied to determine the patient’s critical status of the effects of the coronavirus or its symptoms using heartbeat, cough, temperature and Oxygen concentration (SpO2) that are evaluated using our custom algorithm. Until now, it has been found that many coronavirus patients remain asymptomatic, but in case of known symptoms, a person can be quickly identified with our device. It also allows doctors to examine their patients without the need for physical direct contact with them to reduce the possibility of infection. Our solution uses rule-based decision-making based on the physiological data of a person obtained through sensors. These rules allow to classify a person as healthy or having a possibility of infection by the coronavirus. The advantage of using rules for patient’s classification is that the rules can be updated as new findings emerge from time to time. In this article, we explain the details of the sensors, the smartphone application, and the associated IoT framework for real-time, remote screening of COVID-19.

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“…For detection of cough, there are a few approaches that utilize sensors for its detection [52,54,55]. Our supported sensor, the SW-420 is Doppler radar for cough detection.…”

Section: The Hardware Componentsmentioning

confidence: 99%

mentioning

confidence: 99%

An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors

Mukhtar

Rubaiee

Krichen

et al. 2021

IJERPH

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show abstract

“…The use of different technologies, mainly artificial intelligence (AI) and Internet of Things (IoT), have been shown to be extremely important in combating the COVID-19 pandemic. For instance, the use of AI allows monitoring the appropriate social distance to prevent virus proliferation, supporting diagnoses from medical images of patients’ lungs, and even assessing patient’s cough if there is any indication of illness or if it is environmental [ 26 , 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Air–Oxygen Blenders for Mechanical Ventilators: A Literature Review

Soares

Almeida

Menezes

et al. 2022

Sensors

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Respiratory diseases are one of the most common causes of death in the world and this recent COVID-19 pandemic is a key example. Problems such as infections, in general, affect many people and depending on the form of transmission they can spread throughout the world and weaken thousands of people. Two examples are severe acute respiratory syndrome and the recent coronavirus disease. These diseases have mild and severe forms, in which patients gravely affected need ventilatory support. The equipment that serves as a basis for operation of the mechanical ventilator is the air–oxygen blender, responsible for carrying out the air–oxygen mixture in the proper proportions ensuring constant supply. New blender models are described in the literature together with applications of control techniques, such as Proportional, Integrative and Derivative (PID); Fuzzy; and Adaptive. The results obtained from the literature show a significant improvement in patient care when using automatic controls instead of manual adjustment, increasing the safety and accuracy of the treatment. This study presents a deep review of the state of the art in air–oxygen benders, identifies the most relevant characteristics, performs a comparison study considering the most relevant available solutions, and identifies open research directions in the topic.

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Cough Detection Using Mobile Phone Accelerometer and Machine Learning Techniques

Ali

Khan²,

Zia³

2022

Springer Series on Bio- And Neurosystems

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Exploration of Cough Recognition Technologies Grounded on Sensors and Artificial Intelligence

Cited by 5 publications

References 45 publications

An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors

An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors

Air–Oxygen Blenders for Mechanical Ventilators: A Literature Review

Cough Detection Using Mobile Phone Accelerometer and Machine Learning Techniques

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