• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.2
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• pp.68-76
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• 2010

Touch-enabled technology is increasingly being accepted as a main communication interface between human and computers. However, conventional touchscreen technologies, such as resistive overlay, capacitive overlay, and SAW(Surface Acoustic Wave), are not cost-effective for large screens. As an alternative to the conventional methods, we introduce a newly emerging method, an optical imaging touchscreen which is much simpler and more cost-effective. Despite its attractive benefits, optical imaging touchscreen has to overcome some problems, such as heavy computational complexity, intermittent ghost points, and over-sensitivity, to be commercially used. Therefore, we designed a hardware controller for signal processing and multi-coordinate computation, and proposed Infrared-blocked DA(Dark Area) manipulation as a solution. While the entire optical touch control took 34ms with a 32-bit microprocessor, the designed hardware controller can manage 2 valid coordinates at 200fps and also reduce energy consumption of infrared diodes from 1.8Wh to 0.0072Wh.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2727-2732
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• 2014

Recently, the researches of object tracking and recognition using Microsoft's Kinect are being actively studied. In this environment human hand detection and tracking is the most basic technique for human computer interaction. This paper proposes a method of improving the accuracy of the detected hand region's boundary in the cluttered background. To do this, we combine the hand detection results using the skin color with the extracted depth image from Kinect. From the experimental results, we show that the proposed method increase the accuracy of the hand region detection than the method of detecting a hand region with a depth image only. If the proposed method is applied to the sign language or gesture recognition system it is expected to contribute much to accuracy improvement.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.411-417
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• 2021

The field that studies human-computer interaction is called HCI (Human-computer interaction). This field is an academic field that studies how humans and computers communicate with each other and recognize information. This study is a study on hand gesture recognition for human interaction. This study examines the problems of existing recognition methods and proposes an algorithm to improve the recognition rate. The hand region is extracted based on skin color information for the image containing the shape of the human hand, and the center of gravity profile is calculated using principal component analysis. I proposed a method to increase the recognition rate of hand gestures by comparing the obtained information with predefined shapes. We proposed a method to increase the recognition rate of hand gestures by comparing the obtained information with predefined shapes. The existing center of gravity profile has shown the result of incorrect hand gesture recognition for the deformation of the hand due to rotation, but in this study, the center of gravity profile is used and the point where the distance between the points of all contours and the center of gravity is the longest is the starting point. Thus, a robust algorithm was proposed by re-improving the center of gravity profile. No gloves or special markers attached to the sensor are used for hand gesture recognition, and a separate blue screen is not installed. For this result, find the feature vector at the nearest distance to solve the misrecognition, and obtain an appropriate threshold to distinguish between success and failure.

• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.43-54
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• 2018

The aim of this study is to determine the effect of motion sickness on pupil rhythm. Sixteen volunteers of both genders (8 male, 8 female, mean age $25.67{\pm}2.43$ years) experienced VR contents in both 2D and HMD versions for 15 minutes, and their pupillary rhythms were compared. The irregular pattern of the pupillary rhythms, as demonstrated by increasing mean pupil diameter (mPD) and standard deviation of the pupil diameter (sPD), revealed motion sickness after experiencing HMD condition. The pupillary response is strongly related to the cognitive load, and the motion sickness can be interpreted as a change in the cognitive load caused by the increasing volume of visual information that must be processed and the conflict or inconsistency between different sensory modalities. The method proposed in this study could be a non-contact measurement method for the monitoring of motion sickness using a web-camera rather than previous sensor-based methods.