• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.819-821
• /
• 2014

This study is proposed the design method of tag to measure biomedical signal for interfacing with smart phone. The measurable physiological signals are ECG and PPG. On the smart phone by using the measured signals, we have designed the tag that can extract parameters such as heart rate, heart rate distribution, mean blood pressure, arterial stiffness, autonomic nervous balance. By using the estimated medical informations from this tag, One's health status will be able to manage one.

• Molecules and Cells
• /
• v.45 no.2
• /
• pp.65-75
• /
• 2022

Hypothalamus is a brain region that controls food intake and energy expenditure while sensing signals that convey information about energy status. Within the hypothalamus, molecularly and functionally distinct neurons work in concert under physiological conditions. However, under pathological conditions such as in diet-induced obesity (DIO) model, these neurons show dysfunctional firing patterns and distorted regulation by neurotransmitters and neurohormones. Concurrently, resident glial cells including astrocytes dramatically transform into reactive states. In particular, it has been reported that reactive astrogliosis is observed in the hypothalamus, along with various neuroinflammatory signals. However, how the reactive astrocytes control and modulate DIO by influencing neighboring neurons is not well understood. Recently, new lines of evidence have emerged indicating that these reactive astrocytes directly contribute to the pathology of obesity by synthesizing and tonically releasing the major inhibitory transmitter GABA. The released GABA strongly inhibits the neighboring neurons that control energy expenditure. These surprising findings shed light on the interplay between reactive astrocytes and neighboring neurons in the hypothalamus. This review summarizes recent discoveries related to the functions of hypothalamic reactive astrocytes in obesity and raises new potential therapeutic targets against obesity.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1247-1248
• /
• 2022

Due to rapid urbanization and population growth, it has become crucial to analyze the various volumes and characteristics of pedestrian pathways to understand the capacity and level of service (LOS) for pathways to promote a better walking environment. Different indicators have been developed to measure pedestrian volume. The pedestrian level of service (PLOS), tailored to analyze pedestrian pathways based on the concept of the LOS in transportation in the Highway Capacity Manual, has been widely used. PLOS is a measurement concept used to assess the quality of pedestrian facilities, from grade A (best condition) to grade F (worst condition), based on the flow rate, average speed, occupied space, and other parameters. Since the original PLOS approach has been criticized for producing idealistic results, several modified versions of PLOS have also been developed. One of these modified versions is perceived PLOS, which measures the LOS for pathways by considering pedestrians' awareness levels. However, this method relies on survey-based measurements, making it difficult to continuously deploy the technique to all the pathways. To measure PLOS more quantitatively and continuously, researchers have adopted computer vision technologies to automatically assess pedestrian flows and PLOS from CCTV videos. However, there are drawbacks even with this method because CCTVs cannot be installed everywhere, e.g., in alleyways. Recently, a technique to monitor bio-signals, such as electrodermal activity (EDA), through wearable sensors that can measure physiological responses to external stimuli (e.g., when another pedestrian passes), has gained popularity. It has the potential to continuously measure perceived PLOS. In their previous experiment, the authors of this study found that there were many significant EDA responses in crowded places when other pedestrians acting as external stimuli passed by. Therefore, we hypothesized that the EDA responses would be significantly higher in places where relatively more dynamic objects pass, i.e., in crowded areas with low PLOS levels (e.g., level F). To this end, the authors conducted an experiment to confirm the validity of EDA in inferring the perceived PLOS. The EDA of the subjects was measured and analyzed while watching both the real-world and virtually created videos with different pedestrian volumes in a laboratory environment. The results showed the possibility of inferring the amount of pedestrian volume on the pathways by measuring the physiological reactions of pedestrians. Through further validation, the research outcome is expected to be used for EDA-based continuous measurement of perceived PLOS at the alley level, which will facilitate modifying the existing walking environments, e.g., constructing pathways with appropriate effective width based on pedestrian volume. Future research will examine the validity of the integrated use of EDA and acceleration signals to increase the accuracy of inferring the perceived PLOS by capturing both physiological and behavioral reactions when walking in a crowded area.

• KIPS Transactions on Computer and Communication Systems
• /
• v.6 no.6
• /
• pp.271-280
• /
• 2017

In this paper, we propose a new safety system composed of wearable devices, driver's seat belt, and integrating controllers. The wearable device and driver's seat belt capture driver's biological information, while the integrating controller analyzes captured signal to alarm the driver or directly control the car appropriately according to the status of the driver. Previous studies regarding driver's safety from driver's seat, steering wheel, or facial camera to capture driver's physiological signal and facial information had difficulties in gathering accurate and continuous signals because the sensors required the upright posture of the driver. Utilizing wearable sensors, however, our proposed system can obtain continuous and highly accurate signals compared to the previous researches. Our advanced wearable apparatus features a sensor that measures the heart rate, skin conductivity, and skin temperature and applies filters to eliminate the noise generated by the automobile. Moreover, the acceleration sensor and the gyro sensor in our wearable device enable the reduction of the measurement errors. Based on the collected bio-signals, the criteria for identifying the driver's condition were presented. The accredited certification body has verified that the devices has the accuracy of the level of medical care. The laboratory test and the real automobile test demonstrate that our proposed system is good for the measurement of the driver's condition.

• Journal of Biosystems Engineering
• /
• v.35 no.5
• /
• pp.357-365
• /
• 2010

Workers in the agricultural industry have been exposed to many work-related musculoskeletal disorders. So, our objectives in this study were to measure and analyze worker's physiological bio-information to reduce musculoskeletal disorders in relation to agricultural works. We investigated worker's bio-information of physiological signals during the repeated lifting work such as body temperature, heart rate, blood pressure, physical activity, and heart rate variability. Moreover, we analyzed the workloads of lumbar spine during the repeated lifting work using the 3-axis acceleration and angular velocity sensors. The changes of body temperature was not significant, but the mean heart rate increased from 90/min to 116/min significantly during 30 min of repeated lifting work (p<0.05). The average worker's physical activity(energy consumption rate) was 206 kcal/70kg/h during the repeated lifting work. The workers' acute stress index was more than 80, which indicated a stressful work. Also, the maximum shear force on the disk (L5/S1) of a worker's lumbar spine in static state was 500N, and the maximum inertia moment was 139 $N{\cdot}m$ in dynamic state.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.3
• /
• pp.316-322
• /
• 2014

A movie recommendation system is proposed to learn a preference model of a viewer by using multimodal features of a video content and their evoked implicit responses of the viewer in synchronized manner. In this system, facial expression, body posture, and physiological signals are measured to estimate the affective states of the viewer, in accordance with the stimuli consisting of low-level and affective features from video, audio, and text streams. Experimental results show that it is possible to predict arousal response, which is measured by electrodermal activity, of a viewer from auditory and text features in a video stimuli, for estimating interestingness on the video.

• Science of Emotion and Sensibility
• /
• v.1 no.2
• /
• pp.13-22
• /
• 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 the Ergonomics Society of Korea
• /
• v.24 no.1
• /
• pp.9-17
• /
• 2005

81.1% of traffic accidents is attributed to the drivers. In this regard, D/E model is a practical and effective method in terms of the cost and time in evaluating the road hazardousness. To examine the validity of the threshold values by the levels of demand We selected 10 subjects and collected their physiological signals while they were driving on Honam Highway (Jeonju ${\leftrighttarro}$ Hoideog section). Based on the collected data, the hazardous road condition was evaluated using the new threshold values of the effort level determined by cluster analysis. In applying the D/E model, a decision method based on the demand level was suggested, using a traffic accident prediction model. Additionally, the limit value of the effort level was determined using the drivers' physiological signal data collected at the highway. A comparison analysis of the two D/E models revealed no significant difference: The existing method and the clustering method determined 9 and 7 hazardous road zones, respectively, while actual traffic accidents were reported in 6 and 4 zones, respectively among the predicted road hazardous zones. However, the latter method suggested a more scientific and rational basis in determining the limit value of the Effort level. In conclusion, although D/E model has a great merit as a pioneering method to reflect human factors in evaluating the road hazardousness, it is believed that this method could be improved by a more dynamic method that considers the traffic conditions and the individual physiological signal of the drivers simultaneously in determining a better limit.

• Journal of the Korea Computer Graphics Society
• /
• v.22 no.5
• /
• pp.37-47
• /
• 2016

This study evaluate user feedback such as physiological response and facial expression when subjects play a social decision making game with interactive virtual agent partners. In the social decision making game, subjects will invest some of money or credit in one of projects. Their partners (virtual agents) will also invest in one of the projects. They will interact with different kinds of virtual agents which behave reciprocated or unreciprocated behavior while expressing socially affective facial expression. The total money or credit which the subject earns is contingent on partner's choice. From this study, I observed that subject's appraisal of interaction with cooperative/uncooperative (or friendly/unfriendly) virtual agents in an investment game result in increased autonomic and somatic response, and that these responses were observed by physiological signal and facial expression in real time. For assessing user feedback, Photoplethysmography (PPG) sensor, Galvanic skin response (GSR) sensor while capturing front facial image of the subject from web camera were used. After all trials, subjects asked to answer to questions associated with evaluation how much these interaction with virtual agents affect to their appraisals.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.10
• /
• pp.887-895
• /
• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.