FluSense

Hossain, Md. Farhad; Lover, Andrew A.; Corey, George A.; Reich, Nicholas G.; Rahman, Tauhidur

doi:10.1145/3381014

Cited by 71 publications

(39 citation statements)

References 33 publications

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“…A portable device, called FluSense [54,58], was developed by the University of Massachusetts Amherst. Figure 5 shows the components of the FluSense machine that was operated by an AI-based neural network that can real-time identify cough and crowd size and directly evaluate and collect data for flu-like diseases, such as COVID-19.…”

Section: Observing Covid-19 Through Ai-based Cough Sound Analysismentioning

confidence: 99%

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

Alafif

Tehame

Bajaba

et al. 2021

IJERPH

124

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With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

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Section: Observing Covid-19 Through Ai-based Cough Sound Analysismentioning

confidence: 99%

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

Alafif

Tehame

Bajaba

et al. 2021

IJERPH

124

View full text Add to dashboard Cite

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“…In that study, FluSense collected and analyzed 21 million non-speech audio samples and around 350,000 waiting room thermal images. The study (21) showed that FluSense accurately predicted the patient daily counts (Pearson correlation coefficient = 0.95). The FluSense platform did not take into consideration all respiratory illnesses nor additional health data.…”

Section: Related Workmentioning

confidence: 92%

“…FluSense uses a thermal camera, a microphone array and a neural computing engine to characterize cough sound changes of individuals waiting in hospitals in a real-time manner. The researchers conducted a 7-month study in four public waiting areas equipped with FluSense within the hospital of a large university from December 2018 to July 2019 (21). In that study, FluSense collected and analyzed 21 million non-speech audio samples and around 350,000 waiting room thermal images.…”

Section: Related Workmentioning

confidence: 99%

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework

et al. 2021

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Respiratory symptoms can be caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms: coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases, such as the COVID-19 pandemic. Among the factors that contributed to the spread of the COVID-19 pandemic were the late diagnosis or misinterpretation of COVID-19 symptoms as regular flu-like symptoms. Research has shown that one of the possible differentiators of the underlying causes of different respiratory diseases could be the cough sound, which comes in different types and forms. A reliable lab-free tool for early and accurate diagnosis, which can differentiate between different respiratory diseases is therefore very much needed, particularly during the current pandemic. This concept paper discusses a medical hypothesis of an end-to-end portable system that can record data from patients with symptoms, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly theoretical solution could play an important part in the early diagnosis.

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“…Among AI-Health related works, some researchers have tried to predict the sickness trend of specific areas [33], to develop crowd counting and density estimation methodologies in public places [34], or to determine the distance of individuals from the popular swarms [35] using a combination of visual and geo-location cellular information. However, such research works suffer from challenges such as skilled labour or the cost of designing and implementing the infrastructures.…”

Section: Ai-based Researchmentioning

confidence: 99%

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

2020

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Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-m physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network (DNN) model for automated people detection in the crowd in indoor and outdoor environments using common CCTV security cameras. The proposed DNN model in combination with an adapted inverse perspective mapping (IPM) technique and SORT tracking algorithm leads to a robust people detection and social distancing monitoring. The model has been trained against two most comprehensive datasets by the time of the research—the Microsoft Common Objects in Context (MS COCO) and Google Open Image datasets. The system has been evaluated against the Oxford Town Centre dataset (including 150,000 instances of people detection) with superior performance compared to three state-of-the-art methods. The evaluation has been conducted in challenging conditions, including occlusion, partial visibility, and under lighting variations with the mean average precision of 99.8% and the real-time speed of 24.1 fps. We also provide an online infection risk assessment scheme by statistical analysis of the spatio-temporal data from people’s moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The developed model is a generic and accurate people detection and tracking solution that can be applied in many other fields such as autonomous vehicles, human action recognition, anomaly detection, sports, crowd analysis, or any other research areas where the human detection is in the centre of attention.

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FluSense

Cited by 71 publications

References 33 publications

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

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