Estimação e análise automática de parâmetros de postura ergonômica usando sensor de profundidade
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 th...
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| Format: | master thesis |
| Status: | Published version |
| Publication Date: | 2013 |
| Country: | Brasil |
| Institution: | Universidade Federal do Maranhão (UFMA) |
| Repository: | Biblioteca Digital de Teses e Dissertações da UFMA |
| Language: | Portuguese |
| OAI Identifier: | oai:tede2:tede/1838 |
| Online Access: | http://tedebc.ufma.br:8080/jspui/handle/tede/1838 |
| Access Level: | Open access |
| Keyword: | Ergonomia Avaliação postural Sensor de profundidade Ergonomics Postural assessment Depth sensor |
| Summary: | 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%. |
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