Detection of High Germ Spreading Activities

Eayrs, A.; Min, C. H.

This paper discusses the preliminary process of data collection, data processing, and feature extraction and selection in applications of developing a machine learning model for activity detection, especially the germ spreading activities during the COVID-19 pandemic. In this research, a MetaWear wearable device is used to collect arm and hand motion data from a subject performing various activities. After data was collected from these different activities, the data was processed, and important time-domain features as well as frequency domain features, such as the total energy contained in different frequency bands, were extracted in respect to these different activities with the objective of differentiating between these various activities. Various features were collected to create a feature matrix and input to different Machine Learning algorithms to determine the classification accuracy of the germ spreading activities. Using the ensemble bagged tree model, a classification accuracy of 99.4% was obtained.