# Unstructured Handwashing Recognition using Smartwatch to Reduce Contact Transmission of Pathogens

arXiv: https://arxiv.org/abs/2107.13405 · HTML: https://mlsanity.com/paper/2107.13405/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 57 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Imaging Time-Series to Improve Classification and Imputation](https://mlsanity.com/paper/1506.00327/) (2015)
- [Online Human Activity Recognition using Low-Power Wearable Devices](https://mlsanity.com/paper/1808.08615/) (2018)
- [iWash: A Smartwatch Handwashing Quality Assessment and Reminder System with Real-time Feedback in the Context of Infectious Disease](https://mlsanity.com/paper/2009.10317/) (2020)

## Built on by (most cited first, 1 of 1)

- [You Can Wash Hands Better: Accurate Daily Handwashing Assessment with a Smartwatch](https://mlsanity.com/paper/2112.06657/) (2021)

## Abstract (arXiv)

Current guidelines from the World Health Organization indicate that the SARS-CoV-2 coronavirus, which results in the novel coronavirus disease (COVID-19), is transmitted through respiratory droplets or by contact. Contact transmission occurs when contaminated hands touch the mucous membrane of the mouth, nose, or eyes so hands hygiene is extremely important to prevent the spread of the SARSCoV-2 as well as of other pathogens. The vast proliferation of wearable devices, such as smartwatches, containing acceleration, rotation, magnetic field sensors, etc., together with the modern technologies of artificial intelligence, such as machine learning and more recently deep-learning, allow the development of accurate applications for recognition and classification of human activities such as: walking, climbing stairs, running, clapping, sitting, sleeping, etc. In this work, we evaluate the feasibility of a machine learning based system which, starting from inertial signals collected from wearable devices such as current smartwatches, recognizes when a subject is washing or rubbing its hands. Preliminary results, obtained over two different datasets, show a classification accuracy of about 95% and of about 94% for respectively deep and standard learning techniques.