• Science of Emotion and Sensibility
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• v.20 no.1
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• pp.17-30
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• 2017

Lots of interdisciplinary studies for fusion of high technologes and the other areas of research had been tried in these days. In sports training area, high technologies like a vital sign sensor or an accelerometer were adopted as training tools to improve the performance of the sports players. The purpose of this study is finding the proper locations on the human body for attaching the motion sensors in order to develop a smart sportswear which could be helpful in training players. The rowing was selected as a subject sport as lots of movements of the joint on human body could be seen in rowing motions. The players of rowing could be devided into two weight divisions, the lightweight and the heavyweight. In this study, the change rates of distance between markers on human skin as the players moved were took on the back, the elbow, the hip and the knee area on human body by 3D motion capturing system. The distances between markers and the differences between the lightweight and heavyweight were analyzed. Finally, this study provided the guide lines for designing a motion sensing smart sportswear.

• Journal of Sensor Science and Technology
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• v.26 no.2
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• pp.128-134
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• 2017

Until now, neuropsychological tests can diagnose the brain dysfunction, however, cannot distinguish the objective data of experiment enough to distinguish the relationships between brain dysfunction and cerebropathia. In this paper, an automatic cognitive test equipment system with 6-axis motion sensors was proposed for the automation of neuropsychological tests. Fist-Edge-Palm(FEP) test and Go-no go test were used to evaluate motor programming of frontal lobe. The motion data from the specially designed motion glove are transmitted wirelessly to a computer to detect the gestures automatically. The healthy 20 and 11 persons are investigated for the FEP and Go-No go test, respectively. The recognition rates of gestures of FEP and Go-No go test are min. 91.38% and 89.09%. In conclusion, the automations of cognitive tests are successful to diagnose the brain diagnostics quantitatively.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.440-446
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• 2007

현재 대부분의 모바일 장치들엔 디지털 카메라가 설치되어 있으며 거대한 양의 이미지 데이터들을 저장할 수 있다. 이렇게 많은 양의 이미지가 저장되어 있을 경우 장치 속의 이미지들에 대한 브라우징을 하기 어려워지며, 그에 걸리는 시간도 증가하게 된다. 특히 모바일 장치의 경우 화면의 크기가 작으며, 일반 컴퓨터와 비교하여 부자연스럽고, 불편한 인터페이스를 갖고 있어 어려움을 더욱 증가 시킨다. 우리는 이를 해결하기 위해 3차원 가시화 방법과 모션 센싱을 이용한 인터페이스를 제안하고, 제안된 가시화 방법과 인터페이스의 조합을 통해 모바일 장치에서의 효과적인 이미지 브라우징 방법을 모색해 보았다.

• Journal of the HCI Society of Korea
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• v.3 no.1
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• pp.49-56
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• 2008

Most recent mobile devices can store a massive amount of images. However, the typical user interface of mobile devices, such as a small-size 2D display and discrete-input buttons, make the browsing and manipulation of images cumbersome and time-consuming. As an alternative, we adopt motion-based interaction along with a 3D layout of images, expecting such an intuitive and natural interaction may facilitate the tasks. We designed and implemented a motion-based interaction scheme for image browsing using an ultra mobile PC, and evaluated and compared its usability to that of the traditional button-based interaction. The effects of data layouts (tiled and fisheye cylindrical layouts) were also investigated to see whether they can enhance the effectiveness of the motion based interaction.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.5
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• pp.1-11
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• 2008

Wireless Sensor Networks is applied to improvement of life convenience or service like U-City as well as environment pollution, tunnel and structural health monitoring, storm, and earthquake diagnostic system. To increase the usability of sensor data and applicability, mobile devices and their facilities allow the applications of sensor networks to give mobile users and actuators the results of event detection at anytime and anywhere. In this paper, we present MUSNEMO(Multi-sensor centric Ubiquitous Smart sensor NEtwork using Mobile devices) developed system for providing more efficient and valuable information services with a variety of mobile devices and network camera integrated to WSN. Our system is performed based on IEEE 802.15.4 protocol stack. To validate system usability, we built sensor network environments where were equipped with five application sensors such magnetic, photodiode, microphone, motion and vibration. We also built and tested proposed MUSNEMO to provide a novel model for event detection systems with mobile framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1272-1278
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• 2022

The government enacted and promulgated the 'Severe Accident Punishment Act' in January 2021 and is implementing this law. However, the number of occupational accidents in 2021 increased by 10.7% compared to the same period of the previous year. Therefore, safety measures are urgently needed in the industrial field. In this study, BLE Mesh networking technology is applied for safety management of high-risk industrial sites with poor communication environment. The complex sensor AIoT device collects gas sensing values, voice and motion values in real time, analyzes the information values through artificial intelligence LSTM algorithm and CNN algorithm, and recognizes dangerous situations and transmits them to the server. The server monitors the transmitted risk information in real time so that immediate relief measures are taken. By applying the AIoT device and safety management system proposed in this study to high-risk industrial sites, it will minimize industrial accidents and contribute to the expansion of the social safety net.