Detalhes bibliográficos
| Ano de defesa: |
2018 |
| Autor(a) principal: |
Ferretto, Luciano Rodrigo
 |
| Orientador(a): |
De Marchi, Ana Carolina Bertoletti
|
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade de Passo Fundo
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Computação Aplicada
|
| Departamento: |
Instituto de Ciências Exatas e Geociências – ICEG
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
http://tede.upf.br/jspui/handle/tede/1465
|
Resumo: |
Recommender systems are applied in a number of areas, including health in so-called e-Health systems. Most studies focus on nutrition and physical activity recommendations, with one of the goals to improve lifestyle. The purpose of this study were to develop a system for recommending physical activities for hypertensive patients, in order to facilitate the search for a healthy lifestyle. To achieve this goal, a hypertensive user profile model, called HyperModel2PAR, were developed and a hypertensive physical activity recommender system, called Hyper-RecSysPA. The resulting model is composed of 32 elements divided in three groups, used in the modeling of user profiles within the system of generation of recommendations Hyper-RecSysPA. Three cardiologists validated this system at different times. As a result, more than 75% of the recommendations generated were approved. Therefore, it is understood that the objectives of this work were achieved, since Hyper-Model2PAR and Hyper-RecSysPA obtained positive results in evaluations performed by expert users. |
