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面部防护器:一种避免触摸面部的可穿戴系统。

FaceGuard: A Wearable System To Avoid Face Touching.

作者信息

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

机构信息

Applied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.

Clinical Associate Professor, Cornea and Refractive Surgery, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates.

出版信息

Front Robot AI. 2021 Apr 8;8:612392. doi: 10.3389/frobt.2021.612392. eCollection 2021.

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.

摘要

大多数人会无意识地触摸自己的脸,比如挠痒痒或者用手托住下巴。为了减少新型冠状病毒(COVID-19)的传播,公共卫生官员建议不要触摸自己的脸,因为该病毒通过口腔、鼻子和眼睛的黏膜传播。研究发现,学生、上班族、医护人员和火车上的乘客每小时触摸脸部9至23次。本文介绍了FaceGuard,这是一种利用深度学习来预测导致触摸脸部的手部动作,并提供感官反馈以阻止用户触摸脸部的系统。该系统利用惯性测量单元(IMU)来获取表征涉及触摸脸部的手部动作的特征。通过最小化特征工程,使用一维卷积神经网络(1D-CNN)可以有效地对时间序列数据进行分类;1D-CNN滤波器会自动提取IMU数据中的时间特征。因此,基于40名参与者记录的4800次试验数据,开发并训练了一个基于1D-CNN的预测模型。训练数据是在诸如坐、站或走等各种日常活动中涉及触摸脸部的手部动作时收集的。结果表明,虽然触摸脸部所需的平均时间为1200毫秒,但使用少于550毫秒的IMU数据就能实现超过92%的预测准确率。至于感官反应,本文进行了一项心理物理学实验,比较了视觉、听觉和振动触觉这三种感官反馈方式的反应时间。结果表明,与视觉反馈(561.70毫秒)和听觉反馈(520.97毫秒)相比,振动触觉反馈的反应时间(427.3毫秒)明显更短。此外,与视觉反馈相比,振动触觉和听觉反馈的成功率(避免触摸脸部)在统计学上也更高。这些结果证明了预测手部动作并在不到一秒的时间内提供及时感官反馈以避免触摸脸部的可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74c8/8060563/b6a5b5853bc7/frobt-08-612392-g001.jpg

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