Wearable Smart Sensors for Health Security in Transport: The Case of Study of Diabetic Risk Management thought Advanced Data Analysis Approaches Integrated into Enterprise Process Models

The paper is focused on a pilot case of study about the implementation of smart health sensors in the public transportation sector. The case of study involves business processes of different companies working in transport services, garment manufacturing, and smart health Internet of Things (IoT) sensors. Specifically, the proposed work aims to prove how risk management can be controlled through Personal Protective Equipment (PPE) connected to a control room platform. The new risk management process is executed by means of a platform collecting driver data. The mapping of “AS IS” and the “TO BE” processes by means of the Business Process Modeling Notation (BPMN) approach, highlights the improvement of the procedures applied to predict the health risk, by enabling production and monitoring processes. All processes are described by a platform data flow represented by the Unified Modeling Language (UML) Use Case Diagram (UCD) diagram. Digital data is collected into a data warehouse enabling health monitoring processes. As far as concerns the specific risk addressed by this study the models analyzed in this paper are based on algorithms such as Autoregressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM), both able to predict health status and dangerous conditions of the drivers such as hypo- and hyper- glycemia for Diabetes Mellitus cases. The case study has been developed within the framework of Smart District 4.0 (SD 4.0) project.