The examination of Personal Protective Equipment (PPE) to assure the complete integrity of health personnel in contact with infected patients is one of the most necessary tasks when treating patients affected by infectious diseases, such as Ebola. This work focuses on the study of machine vision techniques for the detection of possible defects on the PPE that could arise after contact with the aforementioned pathological patients. A preliminary study on the use of image classification algorithms to identify blood stains on PPE subsequent to the treatment of the infected patient is presented. To produce training data for these algorithms, a synthetic dataset was generated from a simulated model of a PPE suit with blood stains. Furthermore, the study proceeded with the utilization of images of the PPE with a physical emulation of blood stains, taken by a real prototype. The dataset reveals a great imbalance between positive and negative samples; therefore, all the selected classification algorithms are able to manage this kind of data. Classifiers range from Logistic Regression and Support Vector Machines, to bagging and boosting techniques such as Random Forest, Adaptive Boosting, Gradient Boosting and eXtreme Gradient Boosting. All these algorithms were evaluated on accuracy, precision, recall and F1 score; and additionally, execution times were considered. The obtained results report promising outcomes of all the classifiers, and, in particular Logistic Regression resulted to be the most suitable classification algorithm in terms of F1 score and execution time, considering both datasets.
personal protective equipment (ppe); machine vision; class imbalance; synthetic dataset; physical emulation; adaboost; support vector machine (svm); infectious diseases; healthcare