• 감성과학
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• 제4권1호
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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.

• 대한인간공학회지
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• 제21권3호
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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.

• 제어로봇시스템학회논문지
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• 제19권4호
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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.

• 전자공학회논문지D
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• 제35D권7호
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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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• 제38권2호
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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.

• 한국멀티미디어학회논문지
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• 제25권9호
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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.

• 한국산학기술학회논문지
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• 제14권8호
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• pp.3925-3932
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• 2013

감성은 인간의 삶과 밀접한 관련을 가지고 있으며 이는 집중력, 학습능력 등 많은 부분에 영향을 주어 다양한 행동 패턴을 가지게 한다. 따라서 본 논문의 목적은 부정감성을 구분하기 위하여 생체신호를 기반으로 주요한 특징들을 추출하는 것이다. 이를 위해 본 논문에서는 심전도, 뇌파, 피부 온도와 피부전도도를 기반으로 생체신호를 측정한 후, 선형분류기와 유전 알고리즘의 조합으로 정확하고 신속한 알고리즘 개발하고, 주요 특징을 추출하였다. 그 결과, 알고리즘은 최대 96.4%의 정확도를 가짐을 확인할 수 있었고, 추출된 파라미터는 심박변이도의 Mean, RMSSD, NN50과 뇌파의 전두엽 영역에서의 ${\sigma}$파와 ${\alpha}$파의 주파수 파워, 두정엽 영역에서 ${\alpha}$파, ${\beta}$파, ${\gamma}$파와 의 주파수 파워, 그리고 피부온도의 평균과 표준편차 값이었다. 이에 따라 각 각의 생체신호를 기반으로 한 추출 된 특징들은 부정감성의 분류에 있어 중요한 역할을 함을 확인할 수 있었다.

• Healthcare Informatics Research
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• 제24권4호
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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.

• 한국콘텐츠학회논문지
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• 제10권8호
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• pp.168-176
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• 2010

영상을 주의 깊게 시청하는 관객은 보이는 영상의 성격에 따라 여러 심적 변화를 경험한다. 기쁨, 슬픔, 놀람 등 관객이 느낄 수 있는 다양한 감정 상태는 심리학에서 많이 활용되는 제임스 러셀(James Russell)이 정의한 감정 원형모형을 통해 정리된다. 관객이 느낄 수 있는 감정들 중에서도 특히 지그문트 프로이트(Sigmund Freud)가 언급한 언캐니(uncanny)는 기존의 명료한 정서적 개념들의 틈새에 존재하는 첨예한 중간자로 대변된다. 언캐니 현상은 비윤리적으로 인식되는 영상을 시청하는 관객이 보편적으로 느끼는 불쾌한 부분이 쾌로 수용되는 상태, 즉 사회적 금기에 대해 긍정적인 상태이기 때문에 이는 사회윤리적인 해석과 더불어 명확한 수리적 분석이 후행될 때 비로소 첨예한 심리상태로서 분석 가치를 지닌다. 따라서 본 논문에서는 감정 원형모형과 언캐니 상태를 분석하고, 관객의 언캐니 상태에 관한 가설을 설립하여 ECG(Electronic Cardiogram)와 GSR(Galvanic Skin Response) 신호 측청과 같은 생체신호 실험을 수치화한 결과를 러셀의 감정 원형모형을 기반으로 분포, 거리, 시간 등을 분석하는 방향으로 진행된다.

• Fibers and Polymers
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• 제7권4호
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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.