Gesture recognition for Healthcare 4.0: a machine learning approach to reduce clinical infection risks

The management and monitoring of diagnostic routines for the active surveillance of colonization of antibiotic-resistant bacteria require the use of advanced data drivers based on field sensors that characterize various phases of hospital processes. To this aim, this study describes the proof of concept of an integrated system exploiting smart field sensors and a digital management system that utilizes flow diagrams and business process models and notation (BPMN) to optimize hospital processes. The focus is on the development and validation of the smart field sensor based on a vision system, which extensively leverages machine learning algorithms for real-time identification of hand-washing procedures. The novelty of this research is twofold: hands joints are extracted and processed frame-by-frame to extract relevant geometric features, which are the input of three independent Random Forests. Then, the output of the three Random Forests composes the input for a final supervisor classifier (a simple Artificial Neural Network). The overall system prediction accuracy is of 73.3%, which is an encouraging result given the complexity of such gestures and the simplicity of the algorithms adopted. Therefore, the proposed method demonstrates the capability of field sensors to facilitate novel real-time management models in the healthcare sector, aligning with the principles of the Healthcare 4.0 paradigm.