FaceGuard: A Wearable System To Avoid Face Touching
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 mout...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-04-01
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| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2021.612392/full |
| _version_ | 1818739180111396864 |
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| author | Allan Michael Michelin Georgios Korres Sara Ba’ara Hadi Assadi Haneen Alsuradi Rony R. Sayegh Antonis Argyros Mohamad Eid |
| author_facet | Allan Michael Michelin Georgios Korres Sara Ba’ara Hadi Assadi Haneen Alsuradi Rony R. Sayegh Antonis Argyros Mohamad Eid |
| author_sort | Allan Michael Michelin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-18T01:20:44Z |
| format | Article |
| id | doaj.art-b2e23c0205264518ac3e7dd925305f2e |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-12-18T01:20:44Z |
| publishDate | 2021-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-b2e23c0205264518ac3e7dd925305f2e2022-12-21T21:25:50ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442021-04-01810.3389/frobt.2021.612392612392FaceGuard: A Wearable System To Avoid Face TouchingAllan Michael Michelin0Georgios Korres1Sara Ba’ara2Hadi Assadi3Haneen Alsuradi4Rony R. Sayegh5Antonis Argyros6Mohamad Eid7Applied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab EmiratesApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab EmiratesApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab EmiratesApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab EmiratesApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab EmiratesClinical Associate Professor, Cornea and Refractive Surgery, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab EmiratesProfessor at the Computer Science Department (CSD), University of Crete (UoC), Crete, GreeceApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab EmiratesMost 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.https://www.frontiersin.org/articles/10.3389/frobt.2021.612392/fullface touching avoidanceIMU-based hand trackingsensory feedbackvibrotactile stimulationwearable technologies for health care |
| spellingShingle | Allan Michael Michelin Georgios Korres Sara Ba’ara Hadi Assadi Haneen Alsuradi Rony R. Sayegh Antonis Argyros Mohamad Eid FaceGuard: A Wearable System To Avoid Face Touching Frontiers in Robotics and AI face touching avoidance IMU-based hand tracking sensory feedback vibrotactile stimulation wearable technologies for health care |
| title | FaceGuard: A Wearable System To Avoid Face Touching |
| title_full | FaceGuard: A Wearable System To Avoid Face Touching |
| title_fullStr | FaceGuard: A Wearable System To Avoid Face Touching |
| title_full_unstemmed | FaceGuard: A Wearable System To Avoid Face Touching |
| title_short | FaceGuard: A Wearable System To Avoid Face Touching |
| title_sort | faceguard a wearable system to avoid face touching |
| topic | face touching avoidance IMU-based hand tracking sensory feedback vibrotactile stimulation wearable technologies for health care |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2021.612392/full |
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