Estimação adaptativa da bioimpedancia baseada em medidas antropométricas

• Ano de defesa: 2019
• Autor(a) principal: LAGO, Naydson Emmerson Sousa Pereira do
• Orientador(a): SANTANA, Ewaldo Eder Carvalho
• Banca de defesa: SANTANA, Ewaldo Eder Carvalho , BARROS FILHO, Allan Kardec Duailibe , RIBEIRO, Áurea Celeste da Costa , PAUCAR, Vicente Leonardo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Filtro adaptativo; Bioimpedância; Composição corporal; Filtro de Wiener; Adaptive filter; Bioimpedance; Body composition; Wiener filter
• Área do conhecimento CNPq: Engenharia de Software
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/2659

Obesity has a multifactorial etiology, related to genetic predisposition and to environmental and behavioral factors. Its increase has great importance as a public health problem in modern society. Anthropometry is an inexpensive and easy-to-apply method and studies point to elaborated equations for the purpose of predicting body composition using height, weight and waist circumference. To classify body composition as well as risk for disease development, the World Health Organization, 2000 (WHO) proposed classification based on BMI which is represented by the ratio of weight to kilograms to height in square meters. The scientific literature on the body composition of patients with severe obesity is very scarce. The assessment of fat in these patients may contribute to a better understanding of the risks for cardiovascular disease. This proposal aims to develop the structure of an adaptive filter to estimate body bioimpedance using anthropometric measures collected through a sample of students from the public school system. Measurements such as body mass, height and waist circumference were collected for a better analysis. The development of this filter was based on the Wiener filter, used to produce an estimate of a random process. The Wiener filter minimizes the mean square error between the estimated random process and the desired process. Excellent results were obtained with the developed filter, being these analyzed and compared with the data collected.