• Journal of Biosystems Engineering
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• v.23 no.1
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• pp.83-90
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• 1998

In this paper the automatic system for dairy cattle has been desisted such as body temperature measuring unit, feed supplying unit and temperature control unit. Since e disease is strongly related to the body temperature of cattle, early detection of the abnormal temperature would prevent the severe problems which nay occur in dairy farms. An electronic component AD590J is used as temperature sensor for the system, The device is highly robust against the noise since the output signal is the current so it can be applied to a long distance sensing The resolution of signal is 0.1$^{\circ}C$ and the current is 10㎷ Also 12-bit A/D converter is desisted fir interfacing the sensor with a one-chip microprocessor. A temperature measuring experiment using the developed system has been done for measuring the temperature of human beings and the system was proven to be useful for measuring the body temperature of dairy cattle properly. A geared AC motor is used for the feed supplying unit The heater and fm are used as temperature control unit. The feed supplying unit and temperature control unit are well operating in the laboratory experiment.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05b
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• pp.705-710
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• 1998

We describe a multigrid wavelet-based natural pixel (WNP) method for image reconstruction in emission computed tomography (ECT). The ECT is used to identify the tagged radioactive material's position in the body for detection of abnormal tissue such as tumor or cancer, as in SPECT and PET. With ECT methodology in parallel beam mode, we formulate a matrix-based reconstruction method for radionuclide sources in the human body. The resulting matrix for a practical problem is very large and nearly singular. To overcome this ill-conditioning, wavelet transform is considered in this study. Wavelets have inherent de-noising and multiscale resolution properties. Therefore, the multigrid wavelet-based natural pixel (WNP) method is very efficient to reconstruct image from projection data that is noisy and incomplete. We test this multigrid wavelet natural pixel (WNP) reconstruction method with the MCNP generated projection data for diagnosis of the simulated cancerous tumor.

• Communications of the Korean Mathematical Society
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• v.16 no.3
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• pp.459-485
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• 2001

The aim of EIT (electrical impedance tomography) system is to image cross-section conductivity distribution of a human body by means of both generating and sensing electrodes attached on to the surface of the body, where currents are injected and voltages are measured. EIT has been suffered from the severe ill-posedness which is caused by the inherent low sensitivity of boundary measurements to any changes of internal tissue conductivity values. With a limited set of current-to-voltage data, figuring out full structure of the conductivity distribution could be extremely difficult at present time, so it could be worthwhile to extract some necessary partial information of the internal conductivity. We try to extract some key patterns of current-to-voltage data that furnish some core information on the conductivity distribution such s location and size. This overview provides our recent observation on the location search and the size estimation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.64.2-64
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• 2001

In this paper, fabricated is a motion capture and attitude detection system for Head Mounted Display HMD composed of three low-price and low-grade micro gyroscopes and a micro-controller, To calculate attitude of a body, modified INS algorithm is used. Because the micro gyroscope has much bias drift error, scale factor error, and run-to-run bias error, the motion of a body can not be measured exactly if the general INS algorithm and micro gyroscopes are used. To reduce the errors, three accelerometers can be used. In this case, however, the size and power consumption become too large to use in HMD system. The modified INS algorithm use the grid map and the characteristics of the human motions.

• Journal of Sensor Science and Technology
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• v.32 no.1
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• pp.1-9
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• 2023

Recently, the digital healthcare technologies including non-invasive diagnostics based on Internet of Things (IOT) are getting attention. Human exhaled breath contains a variety of volatile organic compounds (VOCs), which can provide information of malfunctions of the body and presence of a specific disease. Detection of VOCs in exhaled breath using gas sensors are easy to use, safe, and cost-effective. However, accurate diagnosis of diseases is challenging because changes in concentration of VOCs are extremely small and lots of body factors directly or indirectly influence to the conditions. To overcome the limitations, highly selective nanosensors and artificial intelligent electronic nose (E-nose) systems have been mainly researched in recent decades. This review provides brief reviews of the recent studies for diabetes and lung cancer diagnosis using nanosensors and E-nose systems.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.423-427
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• 2022

A pressure sensor that mimics the sensing ability of human skin has emerged as high-profile technology because it shows remarkable applications in numerous fields such as robotics, human health monitoring, and artificial prosthetics. Whereas recent pressure sensors have achieved high sensitivity similar to that of human skin, they still show limited detection bandwidth. Moreover, once these e-skin are fabricated, their sensitivity and stiffness are fixed; therefore, they can be used for only limited applications. Our study proposes a new adaptive pressure sensor built with uniform gallium microdroplet-elastomer composite. Based on the phase transition of gallium microdroplets, the proposed sensor undergoes mode transformation, enabling it to have a higher sensitivity and wider detection bandwidth compared with those of human skin. In addition, we succeeded in extending a single adaptive pressure sensor to sensor arrays based on its high uniformity, reproducibility, and large-scale manufacturability. Finally, we designed an adaptive e-skin with the sensor array and demonstrated its applications on health monitoring tasks including blood pulse and body weight measurements.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.179-186
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• 2009

In this paper, we propose a head detection method based on vertical and horizontal pixel histogram analysis in order to overcome drawbacks of the previous head detection approach using Haar-like feature-based face detection. In the proposed method, we create the vertical and horizontal foreground pixel histogram images from the background subtraction image, which represent the number of foreground pixels in the same vertical or horizontal position. Then we extract feature points of a head region by applying Harris corner detection method to the foreground pixel histogram images and by analyzing corner points. The proposal method shows robust head detection results even in the face image covering forelock by hairs or the back view image in which the previous approaches cannot detect the head regions.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.72-78
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• 2022

The aim of this research is to improve the effectiveness of digital media advertising because current advertisements -in digital signage - indiscriminately appeals to the general public rather than to a specific target. In order to deliver efficient and customized advertisement information, an IoT human body detection sensor mounted on digital signage detected human faces and then classified them firstly by gender. The digital signage here is a smart digital signage that can analyze facial signals, discriminate them based on patterns, and apply the extracted data by displaying the corresponding information to the user. In addition, by identifying the customer's location approaching the smart digital signage and displaying the optimized content information for the customer's location through an algorithm, the digital signage can dramatize the advertisement Thus, this is a study meant forimproving information efficiency while reducing noise and driving power waste generated from unnecessary digital information reproduction.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.171-178
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• 2004

Radio frequency (RF) microwave can be used to predict glucose concentration in a sample. This paper presents preliminary results in determining the concentration by measuring relative permittivity in the solutions of distilled water, saline, human serum, and human blood containing glucose. It was shown that the microwave method has larger penetration depth of about 100times of NIR, than NIR technique in measuring glucose concentration for the tissue like a human muscle. The larger penetration depth of the method has advantages because it is more useful to detect glucose in a human body non-invasively. In the experiments, sensitivity for detecting glucose concentration in blood solutions was almost 57mg/dl at the frequency of approximately 5.8GHz.

• ETRI Journal
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• v.44 no.2
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.