Ágape: Framework para detecção de quedas de idosos e suas causas com dispositivos vestíveis

Abstract

The Internet of Things (IoT) allows everyday objects to communicate with the Internet and, with that, more useful services are provided to users. These services can be embedded in various domains, such as health with Internet of Health Things (IoHT), in which smart devices can monitor patients in order to collect data and identify abnormal situations, indicating the occurrence of emergency situations. One of the situations that can be monitored is an elderly fall, a worldwide problem that can have serious consequences, including death. Given this scenario, it is important to detect a fall more quickly so that the sequelae are minimized. However, just detecting the fall is not enough, as they can be originated from health problems that also need to be treated, such as blood pressure problems and diabetes, called intrinsic causes, and that can be monitored by IoT wearable devices. Therefore, a multi-device solution that detects fall events and possible causative factors can help minimize sequelae. Within this scope, studies were found in the literature that aimed to detect falls, but without relating to other aspects of the health of the elderly that can result in falls. In addition, there was a lack of works with software reuse to facilitate the development of applications for the detection of falls that consider multiple devices and that also relate the fall to its probable intrinsic cause. So, this work proposes a framework, reuse artifact that contains codes common to applications in a domain, called Agape, whose objective is to facilitate the development of IoHT applications for fall detection focusing on wearable IoT devices, relating falls with their possible causes. Agape was evaluated in terms of the time required to configure it, the time required to develop an application with its support, in addition to an evaluation based on the Technology Acceptance Model (TAM), in order to identify the perception of usefulness and ease of use of Agape by the participants. The results indicate that the framework reduces the development time of applications to detect falls and their possible causes by 98.09%. In addition, participants indicated that they were able to perceive the usefulness of Ágape as well as found it easy to use.