Eun-Hye Jang

Analysis of physiological signals for recognition of boredom, pain, and surprise emotions

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.

Enhancement in light emission efficiency of Si nanocrystal light-emitting diodes by a surface plasmon coupling

We report an enhancement in light emission efficiency form Si nanocrystal (NC) light-emitting diodes (LEDs) via surface plasmons (SPs) by employing Au nanoparticles (NPs). Photoluminescence intensity of Si NCs with Au NPs was enhanced by 2 factors of magnitude due to the strong coupling of Si NCs and SP resonance modes of Au NPs. The electrical characteristics of Si NC LED were significantly improved, which was attributed to an increase in an electron injection into the Si NCs due to the formation of inhomogeneous Schottky barrier at the SiC-indium tin oxide interface. Moreover, light output power from the Si NC LED was enhanced by 50% due to both SP coupling and improved electrical properties. The results presented here can provide a very promising way to significantly enhance the performance of Si NC LED.

A Study on Analysis of Bio-Signals for Basic Emotions Classification: Recognition Using Machine Learning Algorithms

The most crucial feature of human computer interaction is computers and computer-based applications to infer the emotional states of humans or others human agents based on covert and/or overt signals of those emotional states. In emotion recognition, bio-signals reflect sequences of neural activity induced by emotional events and also, have many technical advantages. The aim of this study is to classify six emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multi-channel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmograph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using five algorithms, linear discriminant analysis, Naïve Bayes, classification and regression tree, self-organization map and support vector machine. The used algorithms were evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. We have obtained recognition accuracy from 42.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Naïve Bayes and linear discriminant analysis were highest (53.9%, 52.7%) and was lowest by support vector machine (39.2%). This means that Naïve Bayes is the best emotion recognition algorithm for basic emotions. To apply to real system, we have to discuss in the view point of testing and this means that it needs to apply various methodologies for the accuracy improvement of emotion recognition in the future analysis.

ACT-R Predictive Model of Korean Text Entry on Touchscreen

Objective: The aim of this study is to predict Korean text entry on touchscreens using ACT-R cognitive architecture. Background: Touchscreen application in devices such as satellite navigation devices, PDAs, mobile phones, etc. has been increasing, and the market size is expanding. Accordingly, there is an increasing interest to develop and evaluate the interface to enhance the user experience and increase satisfaction in the touchscreen environment. Method: In this study, Korean text entry performance in the touchscreen environment was analyzed using ACT-R. The ACT-R model considering the characteristics of the Korean language which is composed of vowels and consonants was established. Further, this study analyzed if the prediction of Korean text entry is possible through the ACT-R cognitive model. Results: In the analysis results, no significant difference on performance time between model prediction and empirical data was found. Conclusion: The proposed model can predict the accurate physical movement time as well as cognitive processing time. Application: This study is useful in conducting model-based evaluation on the text entry interface of the touchscreen and enabled quantitative and effective evaluation on the diverse types of Korean text input interfaces through the cognitive models.

Relationship between Depression Level and Bio-signals by Emotional Stimuli

Recent studies in mental/physical health monitoring have noted to improve health and wellbeing with the help of Information and Communication Technology (ICT) and in particular, application of biosensors has mainly done because signal acquisition by non-invasive sensors is relatively simple as well as bio-signal is less sensitive to social/cultural difference. Prior to developing a depression monitoring system based on non-invasive bio-signals, we examined a relationship of depressive level and changes of biological features during exposure of emotional stimuli. Ninety-six subjects’ depressive level was measured by a self-rating depression scale (SDS). Electrocardiogram (ECG) and photoplethysmograph (PPG) were recorded during six baseline and emotional states (interest, joy, neutral, pain, sadness and surprise) and heart rate (HR) and pulse transit time (PTT) were extracted. Pearson’s correlation was conducted to examine the relation of depressive level and biological features. The results showed that relation of depressive level and HR is positive in emotional states and there is a negative correlation between depressive level and PTT. We identified that they are meaningful biological features related to depression.

A Study on Information Granular-Driven Polynomial Neural Networks

In this study, we introduce a new design methodology of information granular-driven polynomial neural networks (IgPNNs) that is based on multi-layer perceptron with Context-based Polynomial Neurons (CPNs) or Polynomial Neurons (PNs). Our main objective is to develop a methodological design strategy of IgPNNs as follows: (a) The 1st layer of the proposed network consists of Context-based Polynomial Neuron (CPN). In here, CPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Context- based Fuzzy C-Means (C-FCM) clustering method. The context-based clustering supporting the design of information granules is completed in the space of the input data while the build of the clusters is guided by a collection of some predefined fuzzy sets defined in the output space. (b) The proposed design procedure being applied at each layer of IgPNN leads to the selection of preferred nodes of the network (CPNs or PNs) whose local characteristics can be easily adjusted. These options contribute to the flexibility as well as simplicity and compactness of the resulting architecture of the network. For the evaluation of performance of the proposed IgPNNs, we describe a detailed characteristic of the proposed model using a well-known learning machine data.

Prediction of Human Performance Time to Find Objects on Multi-display Monitors using ACT-R Cognitive Architecture

Objective: The aim of this study was to predict human performance time in finding objects on multi-display monitors using ACT-R cognitive architecture. Background: Display monitors are one of the representative interfaces for interaction between people and the system. Nowadays, the use of multi-display monitors is increasing so that it is necessary to research about the interaction between users and the system on multi-display monitors. Method: A cognitive model using ACT-R cognitive architecture was developed for the model-based evaluation on multi-display monitors. To develop the cognitive model, first, an experiment was performed to extract the latency about the where system of ACT-R. Then, a menu selection experiment was performed to develop a human performance model to find objects on multi-display monitors. The validation of the cognitive model was also carried out between the developed ACT-R model and empirical data. Results: As a result, no significant difference on performance time was found between the model and empirical data. Conclusion: The ACT-R cognitive architecture could be extended to model human behavior in the search of objects on multi-display monitors.. Application: This model can help predicting performance time for the model-based usability evaluation in the area of multi-display work environments.