Using Smartwatches to Detect Face Touching.

Chen Bai,Lisa Anthony,Adam Wolach,Mamoun T Mardini,Yu-Peng Chen

doi:10.3390/s21196528

Abstract

Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identify motion signatures that are mapped accurately to face touching. Participants (n = 10, five women, aged 20–83) performed 10 physical activities classified into face touching (FT) and non-face touching (NFT) categories in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Data features were extracted from consecutive non-overlapping windows varying from 2 to 16 s. We examined the performance of state-of-the-art machine learning methods on face-touching movement recognition (FT vs. NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees, and random forest. While all machine learning models were accurate in recognizing FT categories, logistic regression achieved the best performance across all metrics (accuracy: 0.93 ± 0.08, recall: 0.89 ± 0.16, precision: 0.93 ± 0.08, F1-score: 0.90 ± 0.11, AUC: 0.95 ± 0.07) at the window size of 5 s. IAR models resulted in lower performance, where the random forest classifier achieved the best performance across all metrics (accuracy: 0.70 ± 0.14, recall: 0.70 ± 0.14, precision: 0.70 ± 0.16, F1-score: 0.67 ± 0.15) at the window size of 9 s. In conclusion, wearable devices, powered by machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks as it has the potential to limit face touching as a transmission vector.

Highlights

Frequent facial self-touches, primarily during outbreaks, have the potential to be a mechanism of contracting and transmitting respiratory diseases such as the novel coronavirus (COVID-19)
We examined four machine learning algorithms: logistic regression (LR), support vector machine (SVM), decision tree, and random forest, resulting in 8 models
The goal of this study was to examine the effectiveness of smartwatches and machine learning techniques to recognize face touching (FT) motions, which is important during COVID-19 and other respiratory illness outbreaks

Summary

Introduction

Frequent facial self-touches, primarily during outbreaks, have the potential to be a mechanism of contracting and transmitting respiratory diseases such as the novel coronavirus (COVID-19). According to the Centers for Disease Control and Prevention (CDC) [1], droplets coming from coughing or sneezing transmit many of the germs that cause respiratory illness. These germs usually spread through close contact with an infected person or through touching contaminated surfaces and touching mucosal areas such as the mouth, nose, or eyes [2,3]. Biofeedback is a mind–body technique that helps in making individuals aware of their behaviors. Over time, they can learn to self-regulate their unconscious behavior without feedback [4]. The ability to alert individuals about their facial touches even after they occur is still beneficial to regulate these unconscious behaviors

Objectives

Discussion

Conclusion