A clustering-based approach for quality level verification of sanitation procedures in workplaces

With the COVID-19 pandemic outbreak, sanitizing procedures have become fundamental in work environments, where surfaces and objects are frequently touched by multiple people, enhancing the risk of exposure to the disease. To assure safe working conditions, it is of primary importance to assess the adherence of the sanitation activity to the recommended protocols with a certain level of accuracy. In this work, we propose a methodology able to estimate the accuracy level of sanitation procedures by applying clustering techniques on multiple features extracted from wrist-mounted accelerometric sensors measurements.