• Journal of Korean Academy of Nursing
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• v.32 no.5
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• pp.716-726
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• 2002

The purpose of this study was to explore the effects of hand reflexology on the physiological.emotional responses and immunity of the patients with chronic illness. This study looked specifically at patients with chronic renal failure(CRF) and cancer patients. Method: This study was designed as a quasi-experimental nonequivalent control group pre and post test. Subjects were 54 patients who received dialysis and chemotherapy in one hospital. Thirty-one patients were assigned to the experimental group and 23 to the control group. The hand reflexology was applied to both hands of the experiment group for ten minutes each time, 5 times during three days. For data collection, physiological lab levels, immune cells of blood and questionnaires for emotional responses were measured before and after the program. Result: BT of the experiment group was decreased significantly on both of the 1st and the 5th application. PR & BP were decreased significantly on the 1st times, but not 5th times. Hb levels of the experimental group were significantly increased. And emotional responses, vigor and mood scores of the experiment group were significantly increased. B cell & CD19 were increased significantly on the experiment group. Suppressor T cell and NK cell showed significant decrease after the program, but no significant differences between the groups. Conclusion: We have found that the hand reflexology helps the chronic patients to improve physiological.emotional responses and the immune reaction. Through this result, the hand reflexology is effective as a intervention of psychoneuroimmunologic function.

• Science of Emotion and Sensibility
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• v.14 no.4
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• pp.645-652
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• 2011

This study examined changes of both psychological and physiological emotional responses according to change of the PMV (predicted mean vote) in the heating and the cooling air conditions. For this purpose, the changes of PMV were induced by the heating and cooling operations of the HVAC (heating, ventilation, and air conditioning) systems. In addition, positive/negative and arousal/relaxation were measured as the participant's psychological emotional responses, and HR (heart rate) was measured as the participant's physiological emotional responses. As a result, in same range of the PMV, both psychological and physiological emotional-responses were changed by air conditioning. It is suggested that occupant's emotional responses would depend on the operational conditions of heating and cooling in indoor thermal environments, and both psychological and physiological emotional response should be considered when occupants try to match the indoor thermal environments to their thermal expectations.

• Science of Emotion and Sensibility
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• v.27 no.1
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• pp.3-12
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• 2024

This study examined affective representation by analyzing physiological responses measured using wearable devices and affective ratings in response to emotional videos. To achieve this aim, a published dataset was reanalyzed using multidimensional scaling to demonstrate affective representation in two dimensions. Cross-participant classification was also conducted to identify the consistency of emotional responses across participants. The accuracy and misclassification in each emotional condition were described by exploring the confusion matrix derived from the classification analysis. Multidimensional scaling revealed that the represented objects, namely, emotional videos, were positioned along the rated valence and arousal vectors, supporting the core affect theory (Russell, 1980). Vector fittings of physiological responses also showed the associations between heart rate acceleration and low arousal, increased heart rate variability and negative and high arousal, and increased electrodermal activity and negative and low arousal. Using the data of behavioral and physiological responses across participants, the classification results revealed that emotional videos were more accurately classified than the chance level of classification. The confusion matrix showed that awe, enthusiasm, and liking, which were categorized as positive, low arousal emotions in this study, were less accurately classified than the other emotions and were misclassified for each other. Through multivariate analyses, this study confirms the core affect theory using physiological responses measured through wearable devices and affective ratings in response to emotional videos.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.705-713
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• 2011

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.225-229
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• 1998

The purpose of the current study was comparative analysis of autonomic and electrocortical responses to passive and active touch of the tektites with different subjective emotional preference. Perspective goal of the project is development of a template for classification of tactile stimuli according to subjective comfort and associated physiological manifestations. The study was carried out on 36 female college students. Physiological signals were acquired by Grass and B10PAC 100 systems with AcqKnowledge III software. Frontal, parietal and occipital EEG (relative power spectrum /percents/ of EEG bands - delta, theta, slow and fast alpha, low and fast beta), and autonomic variables, namely heart rate (HR), respiratory sinus arrhythmia (RSA), pulse transit time (PTT), respiration rate (RSP) and skin conductance parameters (SCL, amplitude, rise time and number of SCRs) were analyzed for rest baseline and stimulation conditions. Analysis of the overall pattern of reaction indicated that autonomic response to tactile stimulation was manifested in a form of moderate HR acceleration, RSP increase, RSA decrease (lowered vagal tone), decreased n and increased electrodermal activity (increased SCL, several SCRs) that reflects general sympathetic activation. Parietal EEG effects (on contra-lateral side to stimulated hand) were featured by short-term alpha-blocking, slightly reduced theta and significantly increased delta and enhanced fast beta activity with few variations across stimuli. The main finding of the study was that most and least preferred textures exhibited significant differences in autonomic (HR, RSP, PTT, SCR, and at less extent in RSA and SCL) and electrocortical responses (delta, slow and fast alpha, fast beta relative power). These differences were recorded both in passive and active stimulation modes, thus demonstrating reproducibility of distinction between most and least emotionally preferred tactile stimuli, suggesting influence of psychological factors, such as emotional property of stimulus, on physiological outcome. Nevertheless, development of sufficiently sensitive .and reliable template for classification of emotional responses to tactile stimulation based on physiological response pattern may require more extensive empirical database.

• Science of Emotion and Sensibility
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• v.1 no.2
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• pp.13-22
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• 1998

The sense of touch has both objective and subjective characteristics. During hand evaluation of the fabrics. psycho physiological processes such as emotion and stimulation. On other site, the mode of touch (passive vs. active) is also capable to modulate somatosensory responses. I.e., suppress somatocensory perception during active electrocortical responses to passive and active touch of the textiles with different subjective emotional preference. The study was carried out on 36 female college students. Physiological signals were acquired by Grass and BIOPAC 100 systems with AcqKnowledge variables, namely heart rate (HR), respiratory sinus arrhythmia (RSA), pulse transit time (PTT), respiration rate (RSP) and skin conductance parameters (SCL, amplitude, risetime and number of SCRs) were analyzed for baseline and stimulation conditions. Analysis was manifested in a form of moderate HR acceleration. RSP increase, RSA decrease (lowered vagal tone), decreased PTT and increased electrodermal activity (increased SCL, several SCRs) that reflects general sympathetic activation. Parietal EEG effects (on contra-lateral side to stimulated hand)were featured by short-term alpha-blocking, slightly reduced theta, significantly increased delta and enhanced fast beta activity with few variations across stimuli. The main finding of the study was that most and least preferred textures exhibited significant differences in autonomic (HR, RSP, PTT, SCR, and at less extent in RSA and SCL) and electrocortical responses (delta, slow and fast alpha, fast beta relative power). These differences were recorded both in passive and active stimulation modes, thus demonstrating reproducibility of distinction between most and least emotionally preferred tactile stimuli, suggesting influence of psychological factors, such as emotional property of stimulus, on physiological outcome.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.513-519
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• 2013

The current vision-based approaches for emotion recognition, such as facial expression analysis, have many technical limitations in real circumstances, and are not suitable for applications that use them solely in practical environments. In this paper, we propose an approach for emotion recognition by combining extrinsic representations and intrinsic activities among the natural responses of humans which are given specific imuli for inducing emotional states. The intrinsic activities can be used to compensate the uncertainty of extrinsic representations of emotional states. This combination is done by using PRMs (Probabilistic Relational Models) which are extent version of bayesian networks and are learned by greedy-search algorithms and expectation-maximization algorithms. Previous research of facial expression-related extrinsic emotion features and physiological signal-based intrinsic emotion features are combined into the attributes of the PRMs in the emotion recognition domain. The maximum likelihood estimation with the given dependency structure and estimated parameter set is used to classify the label of the target emotional states.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.143-147
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• 1998

Autonomic and EEG responses of 38 college students were studied during 60-sec long presentation of International Affective Picture System (IAPS )slides evoking, according to subjective reports, negative (disgust, sadness, surprise) and positive (happiness, exciting) emotional. states. Observed were significant heart rate (HR) deceleration, large skin conductance responses (SCR), moderate respiration frequency slowing, reduction of frontal (F 3, F 4 ) and occipital (O 1, O 2 ) fast alpha, and increases of theta, delta and beta relative spectral power values during the first 30 sec of exposure of IAPS pictures. Analysis carried out to differentiate emotion categories according to autonomic responses indicated that observed HR deceleration was larger in magnitude in surprise and sadness than in disgust, SCR amplitude higher in sadness than in disgust. EEC showed significant differences in theta (F 3, F 4 ) and delta (O 1) power increase in disgust vs. happiness, fast alpha (F 3, F 4 ) power was lower in surprise than in happiness, and slow beta power higher. in happiness than in disgust (0 1). Despite some differences. observed within discrete emotion conditions, overall responses pattern of monitored parameters exhibited similar profiles with few variations, most. obvious. in disgust state, which suggests that affective visual stimulation elicits stereotypical responses in a given passive viewing paradigm. However, the magnitude of physiological responses may vary to certain extent across discrete emotional states making it possible to differentiate among particular experimentally-induced emotional states, e.g., disgust vs. sadness by ANS responses or disgust vs. happiness by EEG measures.

• ETRI Journal
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• v.37 no.6
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• pp.1231-1241
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• 2015

In this study, we investigate the relationship between emotions and the physiological responses, with emotion recognition, using the proposed fuzzy information granulation-based neural network (FIGNN) for boredom, pain, and surprise emotions. For an analysis of the physiological responses, three emotions are induced through emotional stimuli, and the physiological signals are obtained from the evoked emotions. To recognize the emotions, we design an FIGNN recognizer and deal with the feature selection through an analysis of the physiological signals. The proposed method is accomplished in premise, consequence, and aggregation design phases. The premise phase takes information granulation using fuzzy c-means clustering, the consequence phase adopts a polynomial function, and the aggregation phase resorts to a general fuzzy inference. Experiments show that a suitable methodology and a substantial reduction of the feature space can be accomplished, and that the proposed FIGNN has a high recognition accuracy for the three emotions using physiological signals.