BackgroundThe aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals.MethodsThree emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions.ResultsThe result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7 % is obtained by using DFA.ConclusionsThis study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.
Heteroepitaxy of vertically well-aligned ZnO nanowall networks with a honeycomblike pattern on GaN∕c-Al2O3 substrates by the help of a Au catalyst was realized. The ZnO nanowall networks with wall thicknesses of 80–140nm and an average height of about 2μm were grown on a self-formed ZnO thin film during the growth on the GaN∕c-Al2O3 substrates. It was found that both single-crystalline ZnO nanowalls and catalytic Au have an epitaxial relation to the GaN thin film in synchrotron x-ray scattering experiments. Hydrogen-sensing properties of the ZnO nanowall networks have also been investigated.
Novel pure tin monoxide (SnO) nanosheets for use in future functional nanoscale devices were produced on indium tin oxide/glass and SiO 2 /Si substrates by a thermal chemical vapor deposition process without the use of catalysts or a vacuum system. The SnO nanosheets were grown purely on the substrates without the coexistence of other nanostructures. High-resolution transmission electron microscopy investigations revealed that the structurally uniform SnO nanosheets without planar defects that were produced had preferred growth directions of ([110] and ([1 j 10] as well as a single-crystalline tetragonal structure. This work provides a general route for the facile synthesis of single-crystalline SnO nanosheets on arbitrary substrates with single, poly, or amorphous crystal structures. In addition, the selective formation of SnO nanosheets on Pt-deposited patterned SiO 2 /Si substrates was successfully accomplished.
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