Browsing by Author "Premathilaka, M"

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    PublicationOpen Access
    COVID-19 symptom identification using Deep Learning and hardware emulated systems
    (Elsevier, 2023-06-28) Liyanarachchi, R; Wijekoon, J; Premathilaka, M; Vidhanaarachchi, S
    The COVID-19 pandemic disrupted regular global activities in every possible way. This pandemic, caused by the transmission of the infectious Coronavirus, is characterized by main symptoms such as fever, fatigue, cough, and loss of smell. A current key focus of the scientific community is to develop automated methods that can effectively identify COVID-19 patients and are also adaptable for foreseen future virus outbreaks. To classify COVID-19 suspects, it is required to use contactless automatic measurements of more than one symptom. This study explores the effectiveness of using Deep Learning combined with a hardware-emulated system to identify COVID-19 patients in Sri Lanka based on two main symptoms: cough and shortness of breath. To achieve this, a Convolutional Neural Network (CNN) based on Transfer Learning was employed to analyze and compare the features of a COVID-19 cough with other types of coughs. Real-time video footage was captured using a FLIR C2 thermal camera and a web camera and subsequently processed using OpenCV image processing algorithms. The objective was to detect the nasal cavities in the video frames and measure the breath cycles per minute, thereby identifying instances of shortness of breath. The proposed method was first tested on crowd-sourced datasets (Coswara, Coughvid, ESC-50, and a dataset from Kaggle) obtained online. It was then applied and verified using a dataset obtained from local hospitals in Sri Lanka. The accuracy of the developed methodologies in diagnosing cough resemblance and recognizing shortness of breath was found to be 94% and 95%, respectively.
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    PublicationEmbargo
    InCOV Chamber: An IoT based Intelligent Chamber to monitor and identify potential COVID-19 positive patients
    (IEEE, 2021-12-09) Liyanarachchi, R. K; Premathilaka, M; Samarawickrama, H; Thilakasiri, N; Wellalage, S; Wijekoon, J
    COVID-19, the infectious disease with common symptoms such as tiredness, fever, cough, and severe symptoms such as shortness of breath has become a global pandemic that has an enormous negative impact on society. Because of the disease’s negative influence o n o rganizational operations, the entire world is concerned about its spread within their organization. Despite the fact that fever is currently the only symptom used to identify COVID-19 suspects, there may be COVID-19 patients who may not show any signs of fever. The goal of this study is to use an IoT-based chamber to detect potential COVID-19 suspects by taking into account the aforementioned symptoms. When a person enters the chamber, our system employs Neural Networks and Artificial Intelligence (AI) to detect COVID-19 symptoms like Fever, Anosmia, Cough, and Shortness of Breath. The proposed system yields accuracies of 95% for fever detection, 96% for Anosmia detection, and 94% for cough analysis.
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    PublicationEmbargo
    InCOV Chamber: Intelligent Chamber to Detect Potential COVID-19 Positive Patients
    (IEEE, 2022-01-12) Liyanarachchi, R. K; Premathilaka, M; Samarawickrama, H; Thilakasiri, N; Hettiarachchi, N. U; Wellalage, S; Wijekoon, J
    COVID-19, the infectious disease with common symptoms such as tiredness, fever, cough, and severe symptoms such as shortness of breath has become a global pandemic that has an enormous negative impact on society.Due to its adverse impact on the operations of organizations, the entire world is highly concerned about the spread of the disease within their organization. Even though fever is the only symptom considered currently to detect suspects, there may be COVID19 patients without any indications of fever. The purpose of this study is to identify potential COVID-19 suspects by taking the aforementioned symptoms into account with the help of an IoT-based chamber. Once a person enters the chamber, our solution uses Neural Networks and Artificial Intelligence(AI) to identify COVID-19 symptoms such as Fever, Anosmia, Cough, and Shortness of Breath. The proposed system yields accuracies of 95% for fever detection, 96% for anosmia detection and 94% for cough analysis.