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使用带动态时间规整和 K 最近邻算法的仪器化腕带进行面部触摸监测。

Face touch monitoring using an instrumented wristband using dynamic time warping and k-nearest neighbours.

机构信息

Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada.

Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.

出版信息

PLoS One. 2023 Feb 17;18(2):e0281778. doi: 10.1371/journal.pone.0281778. eCollection 2023.

Abstract

One of the main factors in controlling infectious diseases such as COVID-19 is to prevent touching preoral and prenasal regions. Face touching is a habitual behaviour that occurs frequently. Studies showed that people touch their faces 23 times per hour on average. A contaminated hand could transmit the infection to the body by a facial touch. Since controlling this spontaneous habit is not easy, this study aimed to develop and validate a technology to detect and monitor face touch using dynamic time warping (DTW) and KNN (k-nearest neighbours) based on a wrist-mounted inertial measurement unit (IMU) in a controlled environment and natural environment trials. For this purpose, eleven volunteers were recruited and their hand motions were recorded in controlled and natural environment trials using a wrist-mounted IMU. Then the sensitivity, precision, and accuracy of our developed technology in detecting the face touch were evaluated. It was observed that the sensitivity, precision, and accuracy of the DTW-KNN classifier were 91%, 97%, and 85% in controlled environment trials and 79%, 92%, and 79% in natural environment trials (daily life). In conclusion, a wrist-mounted IMU, widely available in smartwatches, could detect the face touch with high sensitivity, precision, and accuracy and can be used as an ambulatory system to detect and monitor face touching as a high-risk habit in daily life.

摘要

控制 COVID-19 等传染病的主要因素之一是防止接触口腔和鼻腔前区域。触摸面部是一种经常发生的习惯性行为。研究表明,人们平均每小时会触摸自己的面部 23 次。受污染的手可能会通过面部触摸将感染传播到身体。由于控制这种自发习惯并不容易,因此本研究旨在开发并验证一种使用基于手腕惯性测量单元(IMU)的动态时间规整(DTW)和 KNN(k-最近邻)技术来检测和监测面部触摸的技术,该技术在受控环境和自然环境试验中进行。为此,招募了 11 名志愿者,并使用手腕 IMU 在受控和自然环境试验中记录他们的手部动作。然后评估我们开发的技术在检测面部触摸时的灵敏度、精度和准确性。观察到,在受控环境试验中,DTW-KNN 分类器的灵敏度、精度和准确性分别为 91%、97%和 85%,在自然环境试验(日常生活)中分别为 79%、92%和 79%。总之,广泛应用于智能手表中的手腕 IMU 可以以高灵敏度、精度和准确性检测面部触摸,并且可以作为一种日常活动监测系统,用于检测和监测日常生活中作为高风险习惯的面部触摸。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034b/9937467/2f88d23ae379/pone.0281778.g001.jpg

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