Equipamento médico assistencial para monitoramento da ingestão de alimentos

• Ano de defesa: 2018
• Autor(a) principal: Barbalho, Ingridy Marina Pierre
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal Rural do Semi-Árido; Brasil; Centro de Ciências Exatas e Naturais - CCEN; UFERSA; Programa de Pós-Graduação em Ciência da Computação
• Palavras-chave em Português: Mobile health; Disfagia; Sistemas inteligentes; Ontologias; Histórico alimentar; Dysphagia; Intelligent systems; Ontologies; Food history; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufersa.edu.br/handle/prefix/861

The use of mobile devices for the continuous monitoring of patients with a particular pathology may benefit in their prevention, diagnosis and treatment. The objective of this work is the construction of a Medical Assistance Equipment (EMA) for the monitoring of patients with pathology related to di culty in the food intake process and/or Oropharyngeal Dysphagia. The EMA works by capturing the movements and sound signals generated during the chewing and swallowing process and, from this, identifies the physical type of the food, classifying it as: i) liquid; ii) pasty; iii) solid. It is important to emphasize the requirements for a construction of EMA were taken from the context of Dysphagia. After a classification, the information is stored generating a food history, with detailed information about the meals performed and the distribution of Dysphagia that the patient is. To this end, a domain ontology was implemented with logical axioms capable of classifying the type of physical material of the swallowed food based on the analysis of the data captured during the swallowing process. In order to analyze the results generated by the EMA, experiments were carried out in a real environment with 10 participants, authorized by the ethics committee under the following opinion number: 2.332.026. Each participant was invited to swallow liquid, pasty and solid foods. The data generated by the participants were analyzed and classified by the developed ontology. At the end, the results presented 100% of correct answers in relation to experiments with solid foods, 80% of correct answers in experiments with liquid foods and 75% of correct answers in relation to experiments with pasty foods. A general analysis of the EMA presented the safety area of 85%. Finally, the EMA provided relevant results regarding correct classification of data. Thus, a medical team can monitor, from a distance, the patient’s evolution onwards, the detailed information available, not EMA, facilitating the monitoring process and improving a quality of life of patients requiring remote monitoring