Unstructured Handwashing Recognition using Smartwatch to Reduce Contact Transmission of Pathogens

TL;DR

This paper explores using smartwatch sensors and machine learning to accurately recognize unstructured handwashing activities, aiming to reduce contact transmission of pathogens like COVID-19.

Contribution

It introduces a novel system that leverages inertial data from smartwatches and machine learning to detect handwashing actions with high accuracy.

Findings

Achieved approximately 95% classification accuracy with deep learning.

Achieved approximately 94% classification accuracy with standard machine learning.

Validated system effectiveness on two different datasets.

Abstract

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.