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Detection of Abnormalities in COVID-19 Infected Human Respiratory System using Accelerometer with the aid of Machine Learning
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| Author:
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, K UDAY KIRAN, SHAIKH SHAKEELA, D KALYAN, Y MADHU, G SAI CHAITANYA, G EKANATH REDDY
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| Abstract:
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Present pandemic situation leads to a lot of research in direction of human respiratory system issues and identifying the abnormalities in it. For real time recognition of respiratory patterns of a patient to monitor the threats to human health, the approach is to adopt human activities recognition (HAR). This paper focused on the respiratory abnormalities in the COVID-19 patient. The proposed method uses a 3-Axis Accelerometer ADXL335 to obtain the Respiratory rate data from a normal human being and a COVID-19 patient using a NodeMcu Microcontroller. This data is being sent to the Cloud based Server ThingsBoard. The data is then applied to a Machine learning algorithm to detect the abnormalities in the COVID-19 patient. The data from the cloud further analyzed by a machine algorithm called One-Class SVM to detect the anomalies in the respiratory data.
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| Keyword:
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Accelerometer; Abnormalities; Machine Learning; Monitoring; One-Class SVM, KNN.
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.04.464
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| Download:
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