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Use of Physiological Data to Analyze and Improve the User Experience
Štefeková, Nina ; Beran, Vítězslav (referee) ; Materna, Zdeněk (advisor)
This thesis is concerned with the research of the physiological symptoms of stress, the design and execution of an experiment that can induce short-term stress. Subsequently, the goal was to create a dataset from the experimentally obtained data and use it to train a machine learning model. Stress detection by such a model can be used to analyze the user experience. The proposed experiment uses a combination of stimuli, consisting of a relaxation part, a Stroop test and a web game that deliberately uses an unfriendly user interface. During it, data was recorded using an Empatica E4 device and then processed by an implemented application into the final dataset. A machine learning model that can detect short-term stress was then trained and evaluated. The KNN algorithm model evaluated by cross-validation achieves an accuracy of 84\% when the subject of the is known prior to the model. For an unknown subject, it is on average 78\%. The thesis provides this model and the resulting dataset, for further use. These results show that short-term stress detection is more challenging without prior knowledge of the subject.
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Physiological Data to Analyze and Improve the User Experience
Štěpánek, Daniel ; Beran, Vítězslav (referee) ; Materna, Zdeněk (advisor)
The goal of this thesis is to obtain dataset of physiological data for user emotions in order to analyze and improve human-computer interaction. This paper proposes of the use Empatica wristband to capture physiological data and Python for data processing. After evaluation, 94 % of intact data was obtained from the expected number of samples. Based on the obtained data, it is possible to better analyze the user experience and thus improve it.
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Physiological Data to Analyze and Improve the User Experience
Štěpánek, Daniel ; Beran, Vítězslav (referee) ; Materna, Zdeněk (advisor)
The goal of this thesis is to obtain dataset of physiological data for user emotions in order to analyze and improve human-computer interaction. This paper proposes of the use Empatica wristband to capture physiological data and Python for data processing. After evaluation, 94 % of intact data was obtained from the expected number of samples. Based on the obtained data, it is possible to better analyze the user experience and thus improve it.
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