The growth of the population in need of health care and with reduced mobility in many countries shows the demand to develop assistive technologies to cater for this public, especially when they require home treatment after being discharged from hospital. In this period of their lives, these people may arouse excessive negative feelings and have little social interaction, as well as symptoms related to emotional illnesses (e.g. depression, bipolar disorder, etc). Thus, an opportunity arises to provide monitoring solutions for people with physical and motor needs using computational technologies to identify the physical and emotional state of these individuals by offering greater user interaction with smart environments, helping to diagnose or predict risk situations for the patient, e.g. signs of depression, falls, etc. However, due to the wide range of sensors that can be found in Smart Home environments and the loss of connection between them at any time, one of the main challenges encountered by researchers is to develop opportunistic and flexible solutions which can self-adjust according to the environment. Thus, middleware platforms are a promising alternative for integrating people, systems, data and various heterogeneous computing and communication devices, as well as covering the interoperability of various applications and services running on these devices. Smart integration and context awareness are some of the challenges practically unexplored in this middleware. Therefore, the aim of this project is to propose smart middleware for the IoT in a case study based on smart health monitoring of individuals in smart homes. The proposed middleware aims at a layered architecture capable of integrating intelligence into adaptive middleware systems, integrating applications dynamically, as well as data, sensors and heterogeneous devices.
News published in Agência FAPESP Newsletter about the scholarship: