Comparative Techniques Using Hierarchical Modelling and Machine Learning for Procedure Recognition in Smart Hospitals

Abstract

6G is one of the key cornerstone elements of the futuristic smart system setup - the others being cloud computing, big data, wearable devices and Artificial Intelligence. Also, smart offices and homes have become even more popular than before, because of the advancement in computer vision and Machine Learning (ML) technologies. Recognition of human actions and situations are fundamental components of such systems, especially in complex environments like healthcare, for example at the dentist clinic, where we need cues such as eye movement to distinguish procedures being undertaken. In this work, we compare models based on hierarchical modelling and machine learning to identify the dental procedure. We used the objects seen while following the eye trajectories and focussed on elements including material used for treatment, equipment involved and the teeth conditions i.e. symptoms. Our experiments showed that using Artificial Neural Network (ANN) increased the accuracy of prediction compared to hierarchical modelling. Our experiments show an improvement in accuracy for each of the constituent parameters i.e., symptom (ANN: 95.58% vs. Hierarchical: 45.68%), material (ANN: 86.32% vs. Hierarchical: 45.18%) and equipment (ANN: 92.65% vs. Hierarchical: 59.39%).