• Science of Emotion and Sensibility
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• v.4 no.1
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• pp.23-31
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• 2001

Psychological and physiological effects from simulator sickness could be an important bias factor for sensibility evaluation. The present experiment investigated the effects of simulator sickness on sensibility evaluation in the controlled condition of driving a car for 60 minutes on a constant speed (60km/h) in graphic simulator. The simulator sickness was measured and analysed for every five minutes using their subjective evaluation and physiological signals. Results of the subjective evaluation showed that there was significant difference between rest and driving condition at 10 minutes from the start of driving, and the level of difference was increased linearly with time. The analysis on central and autonomic nervous systems showed the significant difference between rest and driving conditions after 5 minutes from the start of the driving on the parameters $\alpha$/total and $\beta$/total, and increased level of sympathetic nervous system. But there was no significant difference between different time conditions. The results indicates that physiological changes from simulator sickness can be a bias factor in objective evaluation of human sensibility which also, uses physiological signals. That is, the changes on the parameter $\alpha$/total and $\beta$/total, and on activation level of sympathetic nervous system from simulator sickness can be a bias factor for evaluation of the level of pleasantness and tension. Therefore the effort on improving the analysis by minimizing or eliminating the bias factors should be done for better and accurate sensibility evaluation in simulator environments.

• Journal of the Ergonomics Society of Korea
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• v.21 no.3
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• pp.35-46
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• 2002

In this study, emotion changes were induced by four imaginations- pleasantness, unpleasantness, arousal, relaxation and it was examined using subjective evaluation and analysis of the physiological signals of the central and autonomic nerve systems whether the intended emotions were appropriately achieved, and whether these emotion changes could be distinguished from the analysis of physiological signals. Each of the four imaginations was implemented on 32 subjects for 30 seconds, while that Electroencephalogram (EEG), Eelectrocardiogram (RSP) were measured, and a subjective evaluation was implemented following the completion of the measurement. The analysis of the subjective evaluation revealed that the subjects underwent the four clearly differentiated imaginations, and the pleasantness level was classified into four imagination stages, pleasantness>relaxation>arousal=comfort>unpleasantness, and arousal level was classified into four imagination stages in the order of arousal>unpleasantness${\approx}$pleasantness>comfort>relaxation. The analysis of the EEG revealed that three stages of pleasantness level, pleasantness>relaxation=arousal=comfort>unpleasantness were classified from the values of ${\alpha}/{\alpha}+{\beta}\;and\;{\beta}/{\alpha}+{\beta}$, and about tour distinguishable stages of arousal level were obtained from the autonomic nervous system responses following the order of arousal>unpleasantness${\approx}$pleasantness> comfort> relaxation. It was found that intended emotion could be induced from the imagination, and these induced emotion changes could be differentiated using the physiological signals of the EEG and autonomic nervous system.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.364-371
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• 2013

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.7
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• pp.40-47
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• 1998

This paper presents the physiological signal processing CMOS one chip for transmitting human bodys small electrical signals such as electrocardiogram(EKG) or electromyogram(EMG) and the external system for receiving signals was implemented by the commercial ICs. For simultaneous four-subject four-channel telemetry, a new enfored synchronization techniqeu using infrared bi-directional communication has been proposed. The telemeter IC with the size of 5.1*5.1mm$^{2}$ has the following functions: receiving of command signal, initialization of internal state of all functional blocks, decoding of subject-selection signal, time multiplexing of 4-channel modulated physiological signals, transmitting of telemetry signal to external system and auto power down control. The newly designed synchronized oscillator with low supply voltage dependence in the telemeter IC operates at a supply voltage from 4.6~6.0V and the nonlinearity error of PIM modulator was less than 1.2%F.S(full scale). The power saving block operates at the period of 2.5ms even if the telemetry IC does not receive command signal from external system for a constant time.

• Journal of Biosystems Engineering
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• v.38 no.2
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• pp.163-170
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• 2013

Purpose: The purpose of this study is to provide analysis of physiological, biomechanical responses occurring from the operation to lifting or twist lifting task appears frequently in agricultural work. Methods: This study investigated the changes of physiological factors such as heart rate, heart rate variability (HRV) and biomechanical factors such as physical activity and kinetic analysis in the task of twisting at the waist while lifting. Results: Heart rates changed significantly with the workload. The result indicated that the workload of 2 kg was light intensity work, and the workload of 12 kg was hard intensity work. Physical activity increased as the workload increased both on wrist and waist. Besides, stress index of the worker increased with the workload. Dynamic load to herniated discs was analyzed using inertial sensor, and the angular acceleration and torque increased with the workload. The proposed measurement system can measure the recipient's physiological and physical signals in real-time and analyzed 3-dimensionally according to the variety of work load. Conclusions: The system we propose will be a new method to measure agricultural workers' multi-dimensional signals and analyze various farming tasks.

• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1251-1256
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• 2022

Auscultation of heart sounds using a stethoscope is the basic method to diagnose the cardiovascular disease and observation of abnormalities. However, the heart sound transmitted to the ear through the stethoscope is greatly affected by internal sounds such as organ movement or breathing. In addition, the user's experience significantly influences the accuracy of the auscultation result. Therefore, in this paper, we developed a wearable device that simultaneously measures heart sound and PPG signals for cardiac condition monitoring. The structure of the proposed device is designed to simultaneously measure heart sound and PPG signals when worn on a finger and placed on the chest. A prototype was implemented according to the design structure, and it was confirmed that the performance of measurements and collection for physiological signals was excellent through experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3925-3932
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• 2013

Emotion is closely related to the life of human, so has effect on many parts such as concentration, learning ability, etc. and makes to have different behavior patterns. The purpose of this paper is to extract important features based on physiological signals to recognize negative emotion. In this paper, after acquisition of electrocardiography(ECG), electroencephalography(EEG), skin temperature(SKT) and galvanic skin response(GSR) measurements based on physiological signals, we designed an accurate and fast algorithm using combination of linear discriminant analysis(LDA) and genetic algorithm(GA), then we selected important features. As a result, the accuracy of the algorithm is up to 96.4% and selected features are Mean, root mean square successive difference(RMSSD), NN intervals differing more than 50ms(NN50) of heart rate variability(HRV), ${\sigma}$ and ${\alpha}$ frequency power of EEG from frontal region, ${\alpha}$, ${\beta}$, and ${\gamma}$ frequency power of EEG from central region, and mean and standard deviation of SKT. Therefore, the features play an important role to recognize negative emotion.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.309-316
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• 2018

Objectives: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. Methods: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. Results: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. Conclusions: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.168-176
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• 2010

Audience observing visual media with care experience lots of emotional transition according to characteristics of media. Enjoy, sadness, surprising, etc, a variety of emotional state of audiences is often arranged by James Russell's 'A circumplex model of affect' utilized on psychology. Especially, in various emotions, 'Uncanny' mentioned by Sigmund Freud is represented a sharp medium existing in a crack of clearly emotional conception. Uncanny phenomenon is an emotional state of changing from unpleasant to pleasant on an audience observing visual media is been aware of immoral media generally, therefore, because this is a positive state on a social taboo, we need to analyze with a scientific analysis clearly. Therefore, this study will organize James Russell's 'A circumplex model of affect' and uncanny phenomenon, will be progressed to establish a hypothesis about a state of uncanny on audiences observing visual media and analyze results of the physiological signals experiment based on ECG(Electronic Cardiogram), GSR(Galvanic Skin Response) signals with distribution, distance, and moving time in a circumplex model of affect.

• Fibers and Polymers
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• v.7 no.4
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• pp.446-449
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• 2006

The purpose of this study was to evaluate the comfort of PET clothing treated by UV. The physiological responses of the human body were investigated. Mean skin temperature and physiological signals such as Electroencephalogram (EEG), and heart rate (Electrocardiogram, (ECG)) were examined for 20 minutes during stable wearing conditions. Mean skin temperature was measured every two seconds using Ramanathan's method. Physiological responses were measured using Biopac MP100 series and analyzed using the software, Acqknowledge 3.5.2. Psychological effects were analyzed every five minutes. Comfort of untreated PET clothing decreased with the passage of time. Compared with PET clothing untreated, treated for 30 minutes, and treated for 90 minutes, the analysis of EEG showed that PET clothing treated for 90 minutes was the most comfortable after 20 minutes. In addition, the interval of the heart rate shown on the ECG was the highest in PET clothing treated for 90 minutes. Skin temperature was the lowest in PET treated for 90 minutes. We thus conclude that suitable UV irradiation would improve comfort.