Machine Learning and Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring

The digital twins (DTs) paradigm creates a virtual replica of a physical artifact that represents its real-world status in a virtual space. When implemented in the Internet of Robotic Things (IoRT), it develops a virtual model of the physical robot that receives real-time sensor data, acting like an actual robot. The DTs-based IoRT allows remote patient monitoring (RPM) that improves treatment plans and reduces the burden of gathering vital signs on health carers by automating repeated tasks. However, RPM procedures generate large amounts of sensor data, which are difficult to examine. Furthermore, health carers cannot forecast abnormalities based on health data. Machine Learning (ML) can analyze massive amounts of data and perceive patterns to anticipate anomalous health conditions. This research leverages the advantages of ML and DTs-based IoRT to remotely monitor chronic or infectious patients and predict their future status. The proposed system is an extension of our previously published work. It uses the virtual twin (VT) to navigate the physical twin (PT) for collecting data from the patient-mounted sensors and applies ML techniques to predict health anomalies. It evaluated six ML algorithms to determine the most accurate model. The models were trained and tested by using a dataset combining two public datasets and real-world data collected by healthcare providers. Results confirmed that the K Nearest-Neighbor (KNN) has the best accuracy i.e. 97%. The system was also tested in the clinical setting to collect patient data and the best-performing algorithm (KNN) was used for status prediction, obtaining 98% accuracy.