MOTION - Processo de desenvolvimento de aplicações de Internet of Health Things autoadaptativas baseadas em padrões de movimento - Costa Junior, Evilasio (2023)
Information about a person’s movement, such as gait, speed, posture, and location, can indicate health problems, particularly in older adults. These movement patterns can be monitored using Internet of Things (IoT) systems, which are computational systems that use smart objects connected to the internet and equipped with sensors to monitor data in the environment. Using IoT devices, it is possible to identify movement patterns and use them to monitor a person’s health status. The term Internet of Health Things (IoHT) has been used in the literature to identify IoT solutions for health and several studies have already proposed IoHT systems based on movement data. However, there are still challenges in building these systems, such as the lack of a specific software development process. In addition, two other open challenges are: how to relate sensor data to health problems and how these systems can be less intrusive and more ubiquitous to trigger users, only when necessary. A solution for creating ubiquitous and less intrusive systems can be the construction of self-adaptive IoHT systems. Considering this, this thesis proposes a software development process for self-adaptive IoHT applications based on motion data, entitled MOTION. Three reuse artifacts are also proposed to aid the development of IoHT applications. They are: (i) a classification graph, which relates sensors and health situations to help elicit requirements and design the IoHT application, which can also be used as a knowledge base by the application; (ii) a template to help build adaptation rules; and (iii) a framework in Kotlin to help implement self-adaptive IoT applications for Android devices. The MOTION process is evaluated through an experiment with Information and Communication Technology professionals, who used the process to help the development of two self-adaptive IoHT applications based on motion data. Moreover, the reuse artifacts were evaluated through a proof of concept.