• 대한인간공학회지
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• 제35권5호
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• pp.371-381
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• 2016

Objective:This study aims to evaluate the features of heart rate variability (HRV) and respiratory signals as indices for a driver's drowsiness and waking status in order to develop the classification model for a driver's drowsiness and waking status using those features. Background: Driver's drowsiness is one of the major causal factors for traffic accidents. This study hypothesized that the application of combined bio-signals to monitor the alertness level of drivers would improve the effectiveness of the classification techniques of driver's drowsiness. Method: The features of three heart rate variability (HRV) measurements including low frequency (LF), high frequency (HF), and LF/HF ratio and two respiratory measurements including peak and rate were acquired by the monotonous car driving simulation experiments using the photoplethysmogram (PPG) and respiration sensors. The experiments were repeated a total of 50 times on five healthy male participants in their 20s to 50s. The classification model was developed by selecting the optimal measurements, applying a binary logistic regression method and performing 3-fold cross validation. Results: The power of LF, HF, and LF/HF ratio, and the respiration peak of drowsiness status were reduced by 38%, 22%, 31%, and 7%, compared to those of waking status, while respiration rate was increased by 3%. The classification sensitivity of the model using both HRV and respiratory features (91.4%) was improved, compared to that of the model using only HRV feature (89.8%) and that using only respiratory feature (83.6%). Conclusion: This study suggests that the classification of driver's drowsiness and waking status may be improved by utilizing a combination of HRV and respiratory features. Application: The results of this study can be applied to the development of driver's drowsiness prevention systems.

• 한국컴퓨터정보학회논문지
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• 제17권3호
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• pp.105-111
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• 2012

광용적맥파는 심박과 동맥 혈액의 산소포화도 등을 측정하기 위하여 널리 사용된다. 본 논문에서는 요골동맥에서 780nm와 940nm 적외선 LED를 사용하여 PPG 신호를 측정할 수 있는 모니터링 시스템을 구현하였다. 구현된 시스템은 여러 상황에서 특성이 서로 다른 8종의 LED와 2종의 광전 센서를 조합하여 요골동맥에서 광전용적맥파를 측정하였다. 요골동맥에서 측정된 파형들은 반전되어서 기록된다. 이렇게 반전된 파형은 수축기의 신호는 감소시키고, 확장기의 신호는 증가시킨다. 시스템을 통하여 투과형 방식과 반사형 방식에서 각각의 신호 검출이 적합한 환경과 최적의 센서 조합을 파악하여 모니터링 시스템을 구현하고, 각 조합별 그 결과 피검자의 요골동맥에서 안정적으로 사용할 수 있는 심박신호 측정 시스템을 개발하였다.

• 대한의용생체공학회:의공학회지
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• 제30권5호
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• pp.401-411
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• 2009

In this paper, we correct pulse wave velocity(PWV) with heart-rate and derive regression equations to estimate intima-media thickness(IMT). Widely used methods for diagnosis of arteriosclerosis are IMT and PWV. Arterial wall stiffness determines the degree of energy absorbed by the elastic aorta and its recoil in diastole but there is not correlation between sclerosis and IMT in an existing study. In this study, we will correct PWV with heart-rate and get regression equation to estimate IMT using heart-rate correction index(HCI). We executed experiments for this study. Made up question of physical condition and measured electrocardiogram(ECG), photoplethysmogram (PPG) of finger-tip and toe-tip and ultrasound image of carotid artery. Calculated PWV and IMT using ECG, PPG and ultrasound image. We found that every p-value between PWV and IMT is not significant(<0.05). But p-value between IMT and HCI which is a corrected PWV using heart-rate is significant(>0.01). We use HCI and various measured parameter for estimating regression equation and apply backward estimation to select parameters for regression analysis. Result of backward estimation, found that only HCI is possible to derive proper regression equation of IMT. Relationship between PWV and IMT is the second order. Result of regression equation of E-H PWV is $R^2$=0.735, adj $R^2$=0.711. This is the best correlation value. We calculate error of its analysis for verification of earlobe PWV regression equation. Its result is RMSEP=0.0328, MAPE(%) = 4.7622. Like this regression analysis, we know that HCI is useful parameter and relationship between PWV, HCI and IMT. In addition, we are able to suggest possibility which is that we can get different parameter of prediction throughout just one measurement.

• 대한약침학회지
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• 제19권3호
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• pp.197-206
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• 2016

Introduction: This study aims to investigate the effects of acupuncture stimulation on the radial artery's pressure pulse wave, along with various hemodynamic parameters, and to explore the possible underlying mechanism of pulse diagnosis in healthy participants in their twenties. Methods and analysis: This study is a prospective, single-arm, exploratory clinical study. A total of 25 healthy participants, without regard to gender, in their twenties will be recruited by physicians. Written informed consent will be obtained from all participants. The participants will receive acupuncture once at ST36 on both sides. The radial arterial pulse waves will be measured on the left arm of the subjects by using an applicable pulse tonometric device (KIOM-PAS). On the right arm (appearing twice), electrocardiogram (ECG), photoplethysmogram (PPG), respiration and cardiac output (CO) signals, will be measured using a physiological data acquisition system (Biopac module), while the velocity of blood flow, and the diameter and the depth of the blood vessel will be measured using an ultrasonogram machine on the right arm (appearing twice). All measurements will be conducted before, during, and after acupuncture. The primary outcome will be the spectral energy at high frequencies above 10 Hz ($SE_{10-30Hz}$) calculated from the KIOM-PAS device signal. Secondary outcomes will be various variables obtained from the KIOM-PAS device, ECG, PPG, impedance cardiography modules, and an ultrasonogram machine. Discussion: The results of this trial will provide information regarding the physiological and the hemodynamic mechanisms underlying acupuncture stimulation and clinical evidence for the influence of acupuncture on the pressure pulse wave in the radial artery. Ethics and dissemination: This study was approved by the Institutional Review Board (IRB) of Kyung Hee University's Oriental Medical Center, Seoul, Korea (KOMCIRB-150818-HR-030). The study findings will be published in peer-reviewed journals and presented at national and international conferences. Trial registration number: This trial was registered with the Clinical Research Information Service (CRIS) at the Korea National Institute of Health (NIH), Republic of Korea (KCT0001663), which is a registry in the World Health Organization's (WHO's) Registry Network.

• 대한인간공학회지
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• 제31권2호
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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.