https://github.com/bharathsudharsan/covid-away

Code for paper 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'
https://github.com/bharathsudharsan/covid-away

3d-motion accelerometer barometric-pressure cnn-model covid-19 dataset-creation features-extraction gyroscope iforest local-outlier-factor one-class-classification one-class-svm optimization pickle rotation-vectors sensor-fusion smartwatch tflite visualization

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Code for paper 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'

• Host: GitHub
• URL: https://github.com/bharathsudharsan/covid-away
• Owner: bharathsudharsan
• Created: 2020-06-25T08:45:05.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-07-23T09:52:33.000Z (over 2 years ago)
• Last Synced: 2023-04-26T19:22:45.655Z (almost 2 years ago)
• Topics: 3d-motion, accelerometer, barometric-pressure, cnn-model, covid-19, dataset-creation, features-extraction, gyroscope, iforest, local-outlier-factor, one-class-classification, one-class-svm, optimization, pickle, rotation-vectors, sensor-fusion, smartwatch, tflite, visualization
• Language: Python
• Homepage:
• Size: 102 MB
• Stars: 17
• Watchers: 3
• Forks: 15
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## COVID-away: Hand-to-face 3D Motion Dataset and Models for Smartwatches

### Overview

We humans on average touch our face (eye, nose and mouth) 10-20 times an hour, which is often the primary source of getting infected by a variety of viral infections including seasonal Influenza, Coronavirus, Swine flu, Ebola virus, etc. 

In this work, we have collected a hand-to-face multi-sensor 3D motion dataset and named it COVID-away dataset.

Using our dataset, we trained models that can continuously monitor human arm/hand movement using a wearable device and trigger a timely notification (e.g. vibration) to warn the device users when their hands are moved (unintentionally) towards their face. 

The trained COVID-away models can be easily integrated into an app for smartwatches or fitness bands. 

Evaluation shows that the Minimum Covariance Determinant (MCD) model produces the highest F1-score (0.93) using just the smartwatch’s accelerometer data (39 features).

**Paper:** [https://dl.acm.org/doi/10.1145/3423423.3423433](https://dl.acm.org/doi/10.1145/3423423.3423433)

**Video:** [https://confirm.ie/covid_away/](https://confirm.ie/covid_away/)

**WHO page** [https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/en/covidwho-901451](https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/en/covidwho-901451)

### COVID-away Dataset

As shown below, we recorded the accelerometer, gyroscope, barometric pressure \& rotation vector data for 2071 dynamic hand-to-face movements, performed with various postures (standing, leaning, slouching, etc.) and wrist orientations (variations in Roll, Pitch, and Yaw).

![alt text](https://github.com/bharathsudharsan/COVID-away/blob/master/Covid-away_dataset_building.png)

### Features Extractor

We provide a generic feature extractor for enabling users to extract 10 essential features (shown in below Table) from a single data field (dataset row) in any sensor-based motion dataset. Using this, we compute 102 features for each recorded hand-to-face motion data pattern.

![alt text](https://github.com/bharathsudharsan/COVID-away/blob/master/Table1_feature_vectors.PNG)

### COVID-away Models

We provide the beloy type models trained using the features extracted from our COVID-away Dataset. These models when deployed on smartwatches, instantly warn the users when their hands are moved (un-intentionally) to the face.

- COVID-away One-Class Classification Models include:

  -  One-Class SupportVector Machines (OC-SVM)

  -  Isolation Forest (iForest)

  -  Minimum Covariance Determinant (MCD)

  -  Local Outlier Factor (LOF).

- COVID-away CNNs and their model size & latency optimized versions

If the code is useful, please consider citing Covid-away paper using the below BibTex entry:

```

@inproceedings{Bharathcovidaway,

  author    = {Bharath Sudharsan and John G. Breslin and Muhammad Intizar Ali},

  title     = {Avoid Touching Your Face: A Hand-to-face 3D Motion Dataset (COVID-away) and Trained Models for Smartwatches},

  booktitle = {In 10th International Conference on the Internet of Things Companion (IoT ’20 Companion)},

  publisher = {ACM},

  year      = {2020},

  doi       = {10.1145/3423423.3423433},

}

```

For any clarification/further information please don't hesitate to contact me. Email: [email protected]