|
|
Automatic detection of stress using biological signals
Votýpka, Tomáš ; Kozumplík, Jiří (referee) ; Smíšek, Radovan (advisor)
Bachelor's thesis is focused on stress detection. This thesis defines the concept of stress, analyzes the appropriate biological signals for stress detection, presents databases of biological signals, that were used for stress detection and mentions methods of automatic stress detection. Then, a stress detection program was implemented in the MATLAB software environment. A freely available database of non-EEG signals was used to implement the program. Models classifying stress were created using 4 machine learning methods for binary classification and 3 machine learning methods for classifying 4 psychical states. Efficiency of the classification was summarized in the conclusion of this thesis.
|
|
|
Automatic stress detection using non-EEG biological signals
Malina, Ondřej ; Kolářová, Jana (referee) ; Smíšek, Radovan (advisor)
This work deals with the problem of stress detection using non-EEG biosignals. The first part deals with the definition of stress and related concepts. Describes possible views of the phenomenon of stress, mentions possible causes of stress, as well as physiological and psychological manifestations of short and long-term effects of stress. In addition, this work deals with several different methods used to detect stress with non-EEG signals. For this purpose, a short search of articles dealing with this topic is available in this paper. The last chapter of this work describes the algorithm design using the c-mean fuzzy method for detecting stress values in data obtained form five different non-EEG signals.
|
|
|
Automatic detection of stress using biological signals
Votýpka, Tomáš ; Kozumplík, Jiří (referee) ; Smíšek, Radovan (advisor)
Bachelor's thesis is focused on stress detection. This thesis defines the concept of stress, analyzes the appropriate biological signals for stress detection, presents databases of biological signals, that were used for stress detection and mentions methods of automatic stress detection. Then, a stress detection program was implemented in the MATLAB software environment. A freely available database of non-EEG signals was used to implement the program. Models classifying stress were created using 4 machine learning methods for binary classification and 3 machine learning methods for classifying 4 psychical states. Efficiency of the classification was summarized in the conclusion of this thesis.
|
|
|
Automatic stress detection using non-EEG biological signals
Malina, Ondřej ; Kolářová, Jana (referee) ; Smíšek, Radovan (advisor)
This work deals with the problem of stress detection using non-EEG biosignals. The first part deals with the definition of stress and related concepts. Describes possible views of the phenomenon of stress, mentions possible causes of stress, as well as physiological and psychological manifestations of short and long-term effects of stress. In addition, this work deals with several different methods used to detect stress with non-EEG signals. For this purpose, a short search of articles dealing with this topic is available in this paper. The last chapter of this work describes the algorithm design using the c-mean fuzzy method for detecting stress values in data obtained form five different non-EEG signals.
|
guest ::
