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

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Building Simulation 2024年第6期17卷 1023-1036页

作者： Baotian Chang Jianchao Zhang Yingying Geng Jiarui Li Doudou Miao Nan Zhang Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology Beijing University of TechnologyBeijing100124China

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.

关键词： surface transmission touch behavior film pressure sensor random forest infectious disease

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