Current guidelines from the World Health Organization indicate that the SARS-CoV-2 coronavirus, which results in the novel coronavirus disease (COVID-19), is transmitted through respiratory droplets or by contact. Contact transmission occurs when contaminated hands touch the mucous membrane of the mouth, nose, or eyes so hands hygiene is extremely important to prevent the spread of the SARSCoV-2 as well as of other pathogens. The vast proliferation of wearable devices, such as smartwatches, containing acceleration, rotation, magnetic field sensors, etc., together with the modern technologies of artificial intelligence, such as machine learning and more recently deep-learning, allow the development of accurate applications for recognition and classification of human activities such as: walking, climbing stairs, running, clapping, sitting, sleeping, etc. In this work, we evaluate the feasibility of a machine learning based system which, starting from inertial signals collected from wearable devices such as current smartwatches, recognizes when a subject is washing or rubbing its hands. Preliminary results, obtained over two different datasets, show a classification accuracy of about 95% and of about 94% for respectively deep and standard learning techniques.
翻译:世界卫生组织的现行准则表明,SARS-COV-2 Corona病毒(COVID-19)通过呼吸道滴子或接触传播,通过呼吸道滴子或接触传播;当被污染的手碰到嘴、鼻子或眼睛的粘膜时,接触传播,因此,手卫生对于防止SARSCOV-2和其他病原体的传播极为重要;可磨损装置,如智能观察器、包含加速器、旋转器、磁场感应器等的大规模扩散,以及现代人工智能技术,如机器学习和最近更深的学习,使得能够精确地应用识别和分类人类活动,例如:行走、爬楼梯、跑步、拍拍、坐、睡觉等。在这项工作中,我们评价基于机器学习系统的可行性,从从目前智能观察等可磨损装置所收集的惯性信号开始,确认一个对象在洗手或摩擦手。通过两个不同的数据集获得的初步结果显示分类精确度约为95%和大约94%,分别用于深度和标准学习技术。