FaceMask: a Smart Personal Protective Equipment for Compliance Assessment of Best Practices to Control Pandemic

Disposable and reusable face masks represent one of the key personal protective equipment (PPE) against COVID-19 pandemic and their use in public environments is mandatory in many countries. According to the intended use, there exist different types of masks with varying level of filtration. World Health Organization (WHO) has developed a set of best practices and guidelines to the correct use of this fundamental PPE. Nevertheless, many people tend to neglect wearing the mask in presence of other people and to unintentionally overuse the mask before replacement, which results in increased exposure to airborne infections. This paper proposes the development of a smart wearable computing system, consisting of a reusable face mask augmented with sensing elements and wireless connected to a personal mobile device, to recognize correct positioning of the face and capable to monitor other parameters such as usage time. Specifically, we realized a 3D printed mask prototype with replaceable filter and equipped with a small electronic embedded device. The mask collects internal and external parameters including humidity, temperature, volatile organic compounds (VOC) inside the mask, inertial motion, and external temperature and light. Collected data are transmitted over Bluetooth Low Energy to a smartphone responsible of performing signal pre-processing and position classification. Two machine learning algorithms are compared and obtained results from real experiments showed SVM performed slightly better than Naive Bayes, 98% and 97% accuracy, respectively.