Dataset Open Access

Contact-based temperature, breathing and cough patterns dataset for early COVID-19 symptoms identification

Ali, Omer; Ishak, Mohamad Khairi; Bhatti, Muhammad Kamran Liaquat


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    <dct:title>Contact-based temperature, breathing and cough patterns dataset for early COVID-19 symptoms identification</dct:title>
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    <dcat:keyword>anomaly detection</dcat:keyword>
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    <dct:description>&lt;p&gt;This release features the dataset recorded using our prototype hardware device (contact-based) that collects accelerometer and temperature readings to investigate breathing patterns, personal activity and cough patterns. As fever and cough are considered as two of the most common symptoms for COVID-19, our aim was to focus on these physiological features. In addition, research also states that all COVID-19 contractions showed elevated breathing patterns in patients, that could be easily identifiable from Eupnea state. Therefore, in this research project, we aimed at:&lt;br&gt; 1. Designing a prototype chest-worn device to measure dynamic chest movements (to record breathing and cough patterns)&lt;br&gt; 2. Detect different activity patterns&lt;br&gt; 3. Record temperature variations during idle and active stage&lt;br&gt; 4. Using unsupervised machine learning algorithm to detect anomalies by creating a composite score for (breathing and cough patterns as well as temperature).&amp;nbsp;&lt;/p&gt; &lt;p&gt;This release also features some code examples that were used for data pre-processing, feature identification and anomaly detection using (K-means and DBSCAN algorithms).&amp;nbsp;&lt;/p&gt; &lt;p&gt;It is important to note that this dataset should be considered and further investigated for preliminary exploratory analysis. The data was collected from healthy adults (that did not undergo COVID-19 clinical screening tests). Therefore, it must be clearly identified that this dataset DOES NOT represent positive COVID-19 contractions.&amp;nbsp;&lt;/p&gt;</dct:description>
    <dct:description>The dataset is self explanatory with different fields representing different states of the sensors. If unsure, please feel free to email the authors.</dct:description>
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