Classification Graph to the Internet of Health Things Applications
Evilásio Costa, Rossana M. C. Andrade, Leonardo Sampaio · 2023
The Internet of Things (IoT) can be understood as a network of smart devices connected to the Internet that collect and share data. The Internet of Health Things (IoHT) is an area of research that has been gaining a lot of prominences and includes the use of IoT solutions aimed at monitoring healthcare and improving the health of users of these solutions. The development of IoHT solutions involves several challenges, including the interoperability between different smart devices and different sets of sensors, the difficulties of initial design decisions about which technology to use for the solution, and the development cost for the smart devices, which includes not only the financial cost but also the processing and energetic cost that are important to be considered, since the hardware limitations of these smart devices can impede the execution of some applications. The reuse of software artifacts can help reduce costs and other challenges. In this sense, this work proposes the modeling and implementation of a classification graph that relates different sensors, features, classification algorithms, and health states (or situations), providing a reusable software artifact that can help both the requirements elicitation and design stages. Also, the classification graph can be used as a knowledge base for implementing data analysis modules and predicting health states. We evaluated the proposal through a proof of concept, in which we implemented a classification graph based on the proposed model in a web server, using two different datasets, with data from accelerometer and gyroscope sensors and 30 actions (or final states). The developed system also implements three classification algorithms: an artificial neural network, a decision tree, and a random forest. Moreover, we developed a Web API to execute requests for both the creation and update of the classification graph and the request to download the optimized graph and the trained models by the classification algorithms based on the application requirements that requested the graph.