Evidence map›Paper›PMID 33898529›Full record

ArticleFrontiers in robotics and AI2021

FaceGuard: A Wearable System To Avoid Face Touching.

Allan Michael Michelin, Georgios Korres, Sara Ba'ara, Hadi Assadi, Haneen Alsuradi, Rony R Sayegh, Antonis Argyros, Mohamad Eid

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Using Smartwatches to Detect Face Touching.Sensors (Basel, Switzerland) · 2021
    Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Allan Michael MichelinApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Georgios KorresApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Sara Ba'araApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Hadi AssadiApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Haneen AlsuradiApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Rony R SayeghClinical Associate Professor, Cornea and Refractive Surgery, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates.
Antonis ArgyrosProfessor at the Computer Science Department (CSD), University of Crete (UoC), Crete, Greece.
Mohamad EidApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
New York University Abu Dhabi · AEAbu Dhabi Health Services · AEUniversity of Crete · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

face touching avoidanceIMU-based hand trackingsensory feedbackvibrotactile stimulationwearable technologies for health care

Identifiers

PMID33898529
PMCPMC8060563
OpenAlexW3155727414

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.