Apollo: um modelo para predição de acidentes por causas externas em ambientes inteligentes

Abstract

According to data from the World Health Organization (WHO), around 8% of all deaths in the world, approximately 5 million/year, are the result of external causes. These causes can be intentional, such as homicide with a firearm, or unintentional, with domestic accidents such as falling or electric shock being the most frequent. These accidents mainly affect People with Reduced Autonomy (PRA), such as the elderly, children, and People with Disabilities (PWD). Although protocols and standards in the medical field have evolved to assist in the diagnosis and mapping of these accidents, gaps in effective support for the prevention of these health incidents are still observed. From a technological perspective, the accelerated development of the last decades has provided the application of the Internet of Things, the use of wearables and the development of intelligent environments that contribute to the monitoring of activities of people, identifying patterns or detecting accidents such as falls. However, although the detection of events can help to expedite medical care and minimize the consequences of trauma, this approach follows a post-trauma reactive model. On the other hand, this thesis presents the Apollo model, which predicts accidents based on historical contexts of PRA in intelligent environments. Apollo scientifically contributes to external causes prevention by identifying risks and predicting accidents, applying the ubiquitous care approach in intelligent environments, and with the support of service robots. The Apollo model employs supervised machine learning algorithms for the detection and classification of risks, based on the historical contexts of the PRA. Furthermore, it uses the Hidden Markov Model (HMM) model for accident prediction. In addition, the ApolloOnto ontology was designed to formalize the application domain and structure the processed contexts. Apollo Simulator was implemented to generate synthetic datasets that made the experiments possible. For the evaluation of Apollo’s accuracy, 15 scenarios were modeled based on heuristics and validated by 5 experts. The scenarios evaluated considered the prediction of falls of the elderly, burns of the deaf, electric shock, and drowning of children. Risk detection reached an average F1-score of 97.9 %, while accident prediction achieved na average accuracy of 100 %. The results indicate the feasibility and effectiveness of Apollo in supporting accident prediction.