Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150132
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dc.contributor.authorAdithya Aravinthen_US
dc.date.accessioned2021-06-12T08:48:45Z-
dc.date.available2021-06-12T08:48:45Z-
dc.date.issued2021-
dc.identifier.citationAdithya Aravinth (2021). Machine learning for human factors assessment in virtual environment. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150132en_US
dc.identifier.urihttps://hdl.handle.net/10356/150132-
dc.description.abstractHuman factors are often associated with reducing error, enhancing safety, comfort and increasing productivity. Poor evaluation of human factors in working environments often result in human error which leads to the occurrence of critical accidents. In order to create an efficient, effective and safe working environment for workers and the development of appropriate prevention and mitigation strategies, studies conducted on fatigue levels and its influence on human error has been increasing over the years. Human factors also play a major role in individuals engaged in virtual environments, as the fatigue induced during the tasks carried out would affect the session and overall outcome. Hence, traditional questionnaires were introduced to gauge the levels of fatigue induced in the subjects when they are in a virtual environment. However, questionnaires only can be carried out pre and post experiment or session not giving space to any real-time monitoring of the participant’s fatigue levels for safety and future development reasons. This is where eye tracking data will help to give real time monitoring of fatigue levels. This will give us an easier, quick and efficient analysis of fatigue recognition in working individuals. This system will use raw data collected from Eye-tracking devices and process the data through machine-learning algorithms. Eye tracking however, consists of many different variables with some being reliable and some not. Hence, we will find out which variable in the raw eye tracking data has the highest level of accuracy, reliability and performance levels and also backed up by research for future use to get the most accurate fatigue recognition results.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleMachine learning for human factors assessment in virtual environmenten_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Lipoen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailELPWang@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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