Detalhes bibliográficos
| Ano de defesa: |
2013 |
| Autor(a) principal: |
QUINTANILHA, Darlan Bruno Pontes
 |
| Orientador(a): |
SILVA, Aristófanes Corrêa |
| Banca de defesa: |
Não Informado pela instituição |
| 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: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
http://tedebc.ufma.br:8080/jspui/handle/tede/1838
|
Resumo: |
During a workday, a person can take many positions and require muscle strain that can cause work-related musculoskeletal diseases (MSDs). In this situation, the joints will become worn over a long period of time, causing fatigue, injuries, or in severe cases, can lead to permanent deformation. In this sense, postural analysis is essential to evaluate the activity of a person in a work environment, however the traditional monitoring methods are manual, which can be exhausting, tedious and inefficient. An automated approach using sensors depth, by contrast, can provide valuable information about the behavior related to the activity of the person. In this sense, this work presents a methodology for the purpose of assisting the professional use of the ergonomic assessment methods posture: the 3DSSPP (Three Dimensional Static Strength Prediction Program) and RULA (Rapid Upper Limb Assessment) using a depth sensor to extract information for accurate setting of posture. The estimation and analysis of posture parameters based on two valuation methods chosen presented good results, the RULA method showed an accuracy of 71.67%. |
