Detection and Monitoring of High Germ Spreading Activities Using Wearable Sensors

Eayrs, A.; Min, C. H.

This paper discusses our preliminary study and results on data collection, data processing, feature extraction and classification in developing a machine learning model for detecting daily activities of living, especially the germ spreading activities during the COVID-19 pandemic. In this research, a Mbient Lab MetaWear wearable sensor system is used to collect arm and hand motion data from subjects performing various activities. After data was collected from these different activities, the data was processed. Important statistical time-domain features and frequency domain features, such as the total energy in different frequency bands, were extracted with respect to these different activities to differentiate between them. Various features were collected to create a feature matrix and were used to train different Machine Learning algorithms to determine the germ spreading activity classification accuracy. Using the ensemble bagged tree model, a classification accuracy of 97.0% was obtained. © 2021 IEEE.