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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  <identifier identifierType="DOI">10.5281/zenodo.4537822</identifier>
      <creatorName>Ali, Omer</creatorName>
      <affiliation>Universiti Sains Malaysia</affiliation>
      <creatorName>Ishak, Mohamad Khairi</creatorName>
      <givenName>Mohamad Khairi</givenName>
      <affiliation>Universiti Sains Malaysia</affiliation>
      <creatorName>Bhatti, Muhammad Kamran Liaquat</creatorName>
      <givenName>Muhammad Kamran Liaquat</givenName>
      <affiliation>Department of Electrical Engineering, NFC Institute of Engineering and Technology (NFC IET), Multan, Pakistan</affiliation>
    <title>Contact-based temperature, breathing and cough patterns dataset for early COVID-19 symptoms identification</title>
    <subject>anomaly detection</subject>
    <subject>feature extraction</subject>
    <date dateType="Issued">2021-02-12</date>
  <resourceType resourceTypeGeneral="Dataset"/>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4115561</relatedIdentifier>
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    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&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;</description>
    <description descriptionType="Other">The dataset is self explanatory with different fields representing different states of the sensors. If unsure, please feel free to email the authors.</description>
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