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Abstract(s)
A digitalizaĆ§Ć£o da indĆŗstria tem trazido um novo paradigma de interaĆ§Ć£o entre o operador e a mĆ”quina. Com a introduĆ§Ć£o da robĆ³tica nas linhas de produĆ§Ć£o a interaĆ§Ć£o entre o operador e os robots torna-se frequente levando a que em algumas situaƧƵes seja muito prĆ³xima. Esta evoluĆ§Ć£o representa um enorme desafio nĆ£o apenas do ponto de vista tecnolĆ³gico, mas tambĆ©m do ponto de vista da seguranƧa dos operadores. A monitorizaĆ§Ć£o do comportamento humano e a perceĆ§Ć£o do estado emocional do operador pode ser um fator decisivo na deteĆ§Ć£o precoce e prevenĆ§Ć£o de acidentes. Atualmente, existem diversos meios que podem ser utilizados nessa monitorizaĆ§Ć£o e deteĆ§Ć£o, sendo que os meios nĆ£o invasivos trazem vantagens ao nĆ£o implicar adaptaĆ§Ć£o do operador a mais tecnologias. A utilizaĆ§Ć£o de inteligĆŖncia artificial para detetar as emoƧƵes dos operadores torna-se cada vez mais frequente. Esta monitorizaĆ§Ć£o permite analisar o comportamento dos mesmos e criar uma estimativa da probabilidade de ocorrer um acidente. Operadores que apresentem sinais de stress ou de cansaƧo tem maior risco de ter um acidente. Esta anĆ”lise Ć© obtida atravĆ©s da obtenĆ§Ć£o de imagens da face do operador e, atravĆ©s de algoritmos as imagens sĆ£o classificadas em emoƧƵes. Para alĆ©m da monitorizaĆ§Ć£o das emoƧƵes, tambĆ©m a monitorizaĆ§Ć£o dos movimentos dos corpos Ć© utilizada para rastrear o operador e identificar potenciais acidentes. Na presente dissertaĆ§Ć£o pretende-se combinar estes diferentes tipos de monitorizaĆ§Ć£o de comportamentos de risco, tais como a deteĆ§Ć£o dos estados emocionais associados Ć fadiga e ao stress.
The digitalization of the industry has brought a new paradigm between the operator and the machine. With the introduction of the robotic in the production lines, the interaction of the operator with the machine becomes more frequent and in some situations this interaction may be very close. This evolution represents a big challenge not only from the technology point of view, but also from the point of view of the operator safety. Monitoring the human behaviour and the perception of the operator emotional state could be a decisive factor in early detection and prevention of labour accidents. Currently, there are several means that can be used to monitoring the human behaviour, however the non-invasive means have more advantages by the fact they do not require the operator adaption to new technologies. The artificial intelligence has become more used in monitoring the human emotions. Monitoring the human emotions turns possible to estimate a probability to occur an accident between the operator and the machine. The operators showing stress or tiredness have more probability to have an accident. This process is done capturing images of the operator and through algorithms the images are classified in emotions. Also, the operatorās movements are captured to identify potential accidents. The current work intends to combine different types of monitoring of risk behaviours of the operator, such as the operator emotional state associated with fatigue or stress.
The digitalization of the industry has brought a new paradigm between the operator and the machine. With the introduction of the robotic in the production lines, the interaction of the operator with the machine becomes more frequent and in some situations this interaction may be very close. This evolution represents a big challenge not only from the technology point of view, but also from the point of view of the operator safety. Monitoring the human behaviour and the perception of the operator emotional state could be a decisive factor in early detection and prevention of labour accidents. Currently, there are several means that can be used to monitoring the human behaviour, however the non-invasive means have more advantages by the fact they do not require the operator adaption to new technologies. The artificial intelligence has become more used in monitoring the human emotions. Monitoring the human emotions turns possible to estimate a probability to occur an accident between the operator and the machine. The operators showing stress or tiredness have more probability to have an accident. This process is done capturing images of the operator and through algorithms the images are classified in emotions. Also, the operatorās movements are captured to identify potential accidents. The current work intends to combine different types of monitoring of risk behaviours of the operator, such as the operator emotional state associated with fatigue or stress.
Description
Keywords
InteligĆŖncia artificial RobĆ³tica Aprendizagem mĆ”quina DeteĆ§Ć£o emoƧƵes DeteĆ§Ć£o movimentos IndĆŗstria Artificial intelligence Robotics Machine Learning Emotion detection Movementsā detection Industry