Machine learning enabled film pressure sensor to identify surface contacts:An application in surface transmission of infectious disease

作     者：Baotian Chang Jianchao Zhang Yingying Geng Jiarui Li Doudou Miao Nan Zhang Baotian Chang;Jianchao Zhang;Yingying Geng;Jiarui Li;Doudou Miao;Nan Zhang

作者机构：Beijing Key Laboratory of Green Built Environment and Energy Efficient TechnologyBeijing University of TechnologyBeijing100124China 

出 版 物：《Building Simulation》 (建筑模拟（英文）)

年 卷 期：2024年第17卷第6期

页      面：1023-1036页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 0814[工学-土木工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 

基　　金：the National Natural Science Foundation of China(grant No.52108067) 

主　　题：surface transmission touch behavior film pressure sensor random forest infectious disease 

摘      要：The global prevalence of infectious diseases has emerged as a significant challenge in recent *** transmission is a potential transmission route of most gastrointestinal and respiratory infectious diseases,which is related to surface touch *** observation,the traditional method of surface touching data collection,is characterized by limited accuracy and high labor *** this work,we proposed a methodology based on machine learning technologies aimed at obtaining high-accuracy and low-labor-cost surface touch behavioral data by means of sensor-based contact *** touch sensing device,primarily utilizing a film pressure sensor and Arduino board,is designed to automatically detect and collect surface contact data,encompassing pressure,duration and *** make certain the surface touch behavior and to describe the behavioral data more accurately,six classification algorithms(*** Vector Machine and Random Forest)have been trained and tested on an experimentally available dataset containing more than 500 surface *** classification results reported the accuracy of above 85%for all the six classifiers and indicated that Random Forest performed best in identifying surface touch behaviors,with 91.8%accuracy,91.9%precision and 0.98 *** study conclusively demonstrated the feasibility of identifying surface touch behaviors through film pressure sensor-based data,offering robust support for the calculation of viral load and exposure risk associated with surface transmission.