Owing to a growth in the number of elderly people and those with physical/mental impairments, there has been a comparable increase of studies in the area of Internet of Things (IoT) that seek to monitor health, give assistance to management and enhance the quality of life for this sector of society. For this reason, the aim of an IoT-based approach when adopted in medical environments and smart homes is to provide connectivity between the patients and their surroundings by offering mechanisms to assist in the investigation and prevention of accidents and/or diseases. This gives rise to an opportunity to exploit computational systems to determine the physical and emotional state in real time, of people with some kind of handicap so as to monitor their health. This might involve discovering the behavioral pattern of the users' routine and issuing alerts to the families and/or medical teams with regard to some abnormal event or noting the first signs of the onset of a depression. On the basis of artificial intelligence, it is also possible to enable computing systems to "learn" and be adapted to the situation they are in, for example by learning and adapting themselves to the number of exercises and/or emotional states of the users, in particular circumstances that combine concepts of both IoT and Artificial Intelligence. Thus the purpose of this project is to devise and evaluate a model that can: i.) find out the emotional and physical state of the users; ii.) provide a mechanism that can monitor the everyday activities of the users in an intelligent manner; iii.) take advantage of a flexible approach through the use of IoT sensors; and iv.) provide particular and detailed information about the users through a wide range of solutions where their behavioral patterns are analyzed in a distinct and uniform manner , by allowing a better kind of interaction between the computing devices in the Health Smart Homes (HSH) environment.
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