• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1061-1065
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• 2002

Sensors need to be implanted to obtain necessary information for LVAS (Left Ventricular Assist System) operations. Size of the sensors can prevent them from being implanted in a patient and reliabilities of the sensors are questionable for a long term use. In this wort we utilize a developed pump model to estimate flow and pressure difference across the pump without implanted sensors and present a method to obtain the physiological variables as aorta pressure and left ventricle pressure from the pump model and pulsatility of flow estimate or pressure difference estimate. These estimated variables can be used for LVAS control as an index or indices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1076-1094
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• 2022

Technology for emotion recognition is an essential part of human personality analysis. To define human personality characteristics, the existing method used the survey method. However, there are many cases where communication cannot make without considering emotions. Hence, emotional recognition technology is an essential element for communication but has also been adopted in many other fields. A person's emotions are revealed in various ways, typically including facial, speech, and biometric responses. Therefore, various methods can recognize emotions, e.g., images, voice signals, and physiological signals. Physiological signals are measured with biological sensors and analyzed to identify emotions. This study employed two sensor types. First, the existing method, the binary arousal-valence method, was subdivided into four levels to classify emotions in more detail. Then, based on the current techniques classified as High/Low, the model was further subdivided into multi-levels. Finally, signal characteristics were extracted using a 1-D Convolution Neural Network (CNN) and classified sixteen feelings. Although CNN was used to learn images in 2D, sensor data in 1D was used as the input in this paper. Finally, the proposed emotional recognition system was evaluated by measuring actual sensors.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1147-1154
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• 2020

Nowadays, one of the most common diseases is chronic mental fatigue syndrome. This can be caused by many factors, such as busy life, heavy workload, high population density, and adverse technological impact. Most office workers and students who are sitting all day long while being exposed to this kind of environments are likely to be involved in the mental illness. Therefore, to prevent the illness, it has been highly required to design a device that enables mental fatigue to be monitored continuously without human intervention. This paper proposes a linear regression method to reliably estimating the level of human mental fatigue using wearable physiological sensors, with an estimation error of 0.852. Also, this paper presents an Android application that is able to check mental health conditions in daily life.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.9-15
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• 2019

As the human population increases in the world, the ratio of health doctors is rapidly decreasing. Therefore, it is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies. Usually this happens due to stressful situations during everyday activities including work. This paper presents a machine learning approach to reliably estimating the level of human mental stress using wearable physiological sensors. And also, this paper presents an Android- and Arduino-based stress monitoring and relief system.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.619-622
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• 2009

Multi Cultivation Remote-control System(MCRS) for crpos through characteristics of multi-safe sensors was realized. It was carried out to investigate into the effect of LED Control with the physiological activity of crops(for examples, sprouts). We have also composed a Combined Automatic Control System possible for the control of temperature and humidity at the same time. The applied multi-safe sensors for measurement are blue, green, red, white, yellow leds and humidity sensors, web camera sensors under safe conditions for crops cultivation. And we producted the remote control OS using Linux and defined the characteristics of automatic control about sprouts.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.119-126
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• 2007

This paper presents a study investigating the effects of VR learning style on user attention and memory. The study involved users performed the guided or unguided style learning in the virtual environment while user attention was measured through physiological sensors (EEG, ECG, and GSR) and an eye tracking system. The users experienced the five specific events in a virtual environment associated with different stimuli, while they were given more specific goals during the guided task whereas they were given more goal asking them to actively search for the interesting items during the unguided task. The subject's attentions workload, feelings, memories about VR experience were measured by using a variety of physiological sensors during the task, video analysis, and post test survey. The results showed that the unguided task followed by the guided task made a considerable learning effect by giving a preview effect to the user. Moreover, the guided task drew more user attention and mental workload than the unguided task did.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.760-767
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• 2022

This paper introduces alert fatigue and presents methods to measure alert fatigue by using physiological signals. Alert fatigue is a phenomenon that which an individual is constantly exposed to frequent alarms and becomes desensitized to them. Blind spots are one leading cause of struck-by accidents, which is one most common causes of fatal accidents on construction sites. To reduce such accidents, construction equipment is equipped with an alarm system. However, the frequent alarm is inevitable due to the dynamic nature of construction sites and the situation can lead to alert fatigue. This paper introduces alert fatigue and proposes methods to use physiological signals such as electroencephalography, electrodermal activity, and event-related potential for the measurement of alert fatigue. Specifically, this paper presents how raw data from the physiological sensors measuring such signals can be processed to measure alert fatigue. By comparing the processed physiological data to behavioral data, validity of the measurement is tested. Using preliminary experimental results, this paper validates that physiological signals can be useful to measure alert fatigue. The findings of this study can contribute to investigating alert fatigue, which will lead to lowering the struck-by accidents caused by blind spots.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.172-180
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• 1994

Implantable biotelemetry systems are indispensable tools not only in animal research but also in clinical medicine as such systems enable the acquisition of otherwise unavailable physiological data. We present the manufacture of CMOS IC and its system for implantable multichannel biotelemeter system. The internal circuits of this system are designed not only to achieve as multiple functions and low power dissipation as possible but also to enable continuous measurement of physiological data. Its main functions are to enable continuous measurement of physiological data and to accomplish on-off power swiching of an implantable battery by receiving appropriate commanc signals from an external circuit. The implantable circuits of this system are designed and fabricated on a single silicon chip using $1.5\mu$m n-well CMOS process technology. The total power dissipation of implantable circuits for a continuous operation was 6.7mW and for a stand-by operation was 15.2$\mu$ W. This system used together with approriate sensors is expected to contribute to clinical medicine telemetry system of measuring and wireless transmitting such significant physiological parameters as pressure pH and temperature.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.293-300
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• 2022

Flexible, wearable, and implantable electronic sensors have started to gain popularity in improving the quality of life of sick and healthy people, shifting the future paradigm with high sensitivity. However, conventional technologies with a limited lifespan occasionally limit their continued usage, resulting in a high cost. In addition, traditional battery technologies with a short lifespan frequently limit operation, resulting in a substantial challenge to their growth. Subsequently, utilizing human biomechanical energy is extensively preferred motion for biologically integrated, self-powered, functioning devices. Ideally suited for this purpose are piezoelectric energy harvesters. To convert mechanical energy into electrical energy, devices must be mechanically flexible and stretchable to implant or attach to the highly deformable tissues of the body. A systematic analysis of piezoelectric nanogenerators (PENGs) for personalized healthcare is provided in this article. This article briefly overviews PENGs as self-powered sensor devices for energy harvesting, sensing, physiological motion, and healthcare.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.324-333
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• 2014

In this study, physiological status of locomotive engineers were measured through EEG, ECG, EDA, PPG and respiration signals from 6 subjects to evaluate their arousal status during train operating. Existence of tunnels and mechanical vibration of train using 3-axes acceleration sensors were recorded simultaneously and were correlated with operator's physiological status. As the result of the analyzed subjects' physiological signals, mean SCR was increased in the section where more body movement is required. The RR interval was decreased before and after train stop due to the higher level of mental tension. The intensity of beta wave of EEG was found to be higher before and after train stop and tunnel section due to the increased mental arousal and tension. Therefore, it is expected that the outcomes of the physiological signals explored in this study can be utilized as the quantitative assessment methods for the arousal status to be used for sleepiness prevention system for vehicles operators which can greatly contribute to public transportation system safety.