ArticleFrontiers in robotics and AI2021
FaceGuard: A Wearable System To Avoid Face Touching.
Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases.PloS one · 2024Article
- Impact of a Serious Game (#RedPingüiNO) to Reduce Facial Self-Touches and Prevent Exposure to Pathogens Transmitted via Hands: Quasi-Experimental Intervention.JMIR serious games · 2023Article
- Wireless Body Area Network for Preventing Self-Inoculation Transmission of Respiratory Viral Diseases.Sensors (Basel, Switzerland) · 2023Article
- Face touch monitoring using an instrumented wristband using dynamic time warping and k-nearest neighbours.PloS one · 2023Article
- The suppression of spontaneous face touch and resulting consequences on memory performance of high and low self-touching individuals.Scientific reports · 2022Article
- Using Smartwatches to Detect Face Touching.Sensors (Basel, Switzerland) · 2021Article
- Virtual Reality and Wearable Technologies to Support Adaptive Responding of Children and Adolescents With Neurodevelopmental Disorders: A Critical Comment and New Perspectives.Frontiers in psychology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most people touch their faces unconsciously, for instance to scratch an itch or to rest one's chin in their hands. To reduce the spread of the novel coronavirus (COVID-19), public health officials recommend against touching one's face, as the virus is transmitted through mucous membranes in the mouth, nose and eyes. Students, office workers, medical personnel and people on trains were found to touch their faces between 9 and 23 times per hour. This paper introduces FaceGuard, a system that utilizes deep learning to predict hand movements that result in touching the face, and provides sensory feedback to stop the user from touching the face. The system utilizes an inertial measurement unit (IMU) to obtain features that characterize hand movement involving face touching. Time-series data can be efficiently classified using 1D-Convolutional Neural Network (CNN) with minimal feature engineering; 1D-CNN filters automatically extract temporal features in IMU data. Thus, a 1D-CNN based prediction model is developed and trained with data from 4,800 trials recorded from 40 participants. Training data are collected for hand movements involving face touching during various everyday activities such as sitting, standing, or walking. Results showed that while the average time needed to touch the face is 1,200 ms, a prediction accuracy of more than 92% is achieved with less than 550 ms of IMU data. As for the sensory response, the paper presents a psychophysical experiment to compare the response time for three sensory feedback modalities, namely visual, auditory, and vibrotactile. Results demonstrate that the response time is significantly smaller for vibrotactile feedback (427.3 ms) compared to visual (561.70 ms) and auditory (520.97 ms). Furthermore, the success rate (to avoid face touching) is also statistically higher for vibrotactile and auditory feedback compared to visual feedback. These results demonstrate the feasibility of predicting a hand movement and providing timely sensory feedback within less than a second in order to avoid face touching.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.