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지훈(H. Ji),이동훈(D. H. Lee) 한국재활복지공학회 2015 재활복지공학회논문지 Vol.9 No.1
팔의 사용이 자유롭지 못한 장애인들을 위하여 발의 움직임 검출을 통하여 로봇 팔을 제어할 수 있는 시스템을 구현하였다. 발의 움직임에 대한 영상을 얻기 위하여 양쪽 발 앞에 두 대의 카메라를 설치하였으며, 획득된 영상에 대해 LabView 기반 Vision Assistant를 이용하여 다중 관심영역을 설정한 후, 좌/우영역내에서 검출된 좌/우, 상/하 엣지를 기반으로 발의 움직임을 검출하였다. 좌/우 두발의 영상으로부터 좌/우 엣지와 상/하 엣지 검출 수에 따라 6관절 로봇 팔을 제어할 수 있는 제어용 데이터를 시리얼 통신을 통해 전송한 후 로봇 팔을 발로 상/하, 좌/우 제어할 수 있는 시스템을 구현하였다. 실험 결과 0.5초 이내의 반응속도와 88% 이상의 동작 인식률을 얻을 수 있었다. The system for controlling the robotic arm through the foot motion detection was implemented for the disabled who not free to use of the arm. In order to get an image on foot movement, two cameras were setup in front of both foot. After defining multiple regions of interest by using LabView-based Vision Assistant from acquired images, we could detect foot movement based on left/right and up/down edge detection within the left/right image area. After transferring control data which was obtained according to left/right and up/down edge detection numbers from two foot images of left/right sides through serial communication, control system was implemented to control 6-joint robotic arm into up/down and left/right direction by foot. As a result of experiment, we was able to get within 0.5 second reaction time and operational recognition rate of more 88%.
뇌파 검출을 통한 졸음지표 검출 및 소형 측정 장치 개발
안영준(Y. J. An),이충헌(C. H. Lee),박문규(M. K. Park),지훈(H. Ji),김정원(J. W. Kim),백종현(J. H. Baek),이동훈(D. H. Lee) 한국재활복지공학회 2015 한국재활복지공학회 학술대회논문집 Vol.2015 No.11
In cause total traffic accidents in Korea, drowsy driving has been shown that it is larger factors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. In this study, first as a result of the drowsiness induction experiments of the frontal area 1 channel measurement method for targeting a small number of experimenters, the pattern of brain waves meaning that drowsiness state could be detected by power spectral changes of Alpha waves. miniaturized EEG devices self-develop 1-channel measurement methods and a wireless data transmission system using the LabView program for detecting drowsiness. Based on this, EEG raw data results can be monitored from PC with wireless communications. So, if signal processing algorithm and warning system for drowsiness detection take advantage of development on drowsiness prevention system of drivers may be expected lower to contribute as drowsy driving accident caused death rate.