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
Video: https://confirm.ie/covid_away/
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).
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.
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]