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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  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.4537822", 
  "language": "eng", 
  "title": "Contact-based temperature, breathing and cough patterns dataset for early COVID-19 symptoms identification", 
  "issued": {
    "date-parts": [
  "abstract": "<p>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:<br>\n1. Designing a prototype chest-worn device to measure dynamic chest movements (to record breathing and cough patterns)<br>\n2. Detect different activity patterns<br>\n3. Record temperature variations during idle and active stage<br>\n4. Using unsupervised machine learning algorithm to detect anomalies by creating a composite score for (breathing and cough patterns as well as temperature).&nbsp;</p>\n\n<p>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).&nbsp;</p>\n\n<p>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.&nbsp;</p>", 
  "author": [
      "family": "Ali, Omer"
      "family": "Ishak, Mohamad Khairi"
      "family": "Bhatti, Muhammad Kamran Liaquat"
  "note": "The dataset is self explanatory with different fields representing different states of the sensors. If unsure, please feel free to email the authors.", 
  "version": "1.1", 
  "type": "dataset", 
  "id": "4537822"
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