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로봇 지원 원격 초음파 영상진단을 위한 마스터-슬레이브 시스템의 개발
서준호,조장호,권오원,Seo, Joonho,Cho, Jang Ho,Kwon, Ohwon 한국로봇학회 2017 로봇학회 논문지 Vol.12 No.4
In this paper, we introduce a robot-assisted medical diagnostic system that enables remote ultrasound (US) imaging to be applied to the conventional telemedicine, which has been possible only with interviewing or a visual exam. In particular, a master-slave robot system is developed that ultrasonic diagnosis specialist can control the position and orientation of US probe in the remote place. The slave robot is designed to be compact, lightweight, and hand-held so that it can easily transfer to the remote healthcare center. Moreover, 6-degree-of-freedom (DOF) probe motion is possible by the robot design based on Stewart platform. The master device is also based on a similar structure of the slave robot. To connect master and slave system in the wide area network (WAN) environment, a hardware CODEC was developed. In this paper, we introduce the detail of each component and the results of the recent experiments conducted in the remote sites by the developed robotic ultrasound imaging system.
운전자의 체압 분포 및 시트변형에 대한 정량화 측정시스템
권영은 ( Yeong-eun Kwon ),김윤영 ( Yun-young Kim ),이용구 ( Yong-goo Lee ),이동규 ( Dongkyu Lee ),권오원 ( Ohwon Kwon ),강신원 ( Shin-won Kang ),이강호 ( Kang-ho Lee ) 한국센서학회 2018 센서학회지 Vol.27 No.6
Proper seat design is critical to the safety, comfort, and ergonomics of automotive driver’s seats. To ensure effective seat design, quantitative methods should be used to evaluate the characteristics of automotive seats. This paper presents a system that is capable of simultaneously monitoring body pressure distribution and surface deformation in a textile material. In this study, a textile-based capacitive sensor was used to detect the body pressure distribution in an automotive seat. In addition, a strain gauge sensor was used to detect the degree of curvature deformation due to high-pressure points. The textile-based capacitive sensor was fabricated from the conductive fabric and a polyurethane insulator with a high signal-to-noise ratio. The strain gauge sensor was attached on the guiding film to maximize the effect of its deformation due to bending. Ten pressure sensors were placed symmetrically in the hip area and six strain gauge sensors were distributed on both sides of the seat cushion. A readout circuit monitored the absolute and relative values from the sensors in real-time, and the results were displayed as a color map. Moreover, we verified the proposed system for quantifying the body pressure and fabric deformation by studying 18 participants who performed three predefined postures. The proposed system showed desirable results and is expected to improve seat safety and comfort when applied to the design of various seat types. Moreover, the proposed system will provide analytical criteria in the design and durability testing of automotive seats.
카메라 영상과 딥러닝을 이용한 의수로봇 제어 시스템과 파지대상 선정
박해준(Haejune Park),안보현(Bohyeon An),백준민(Junmin Baek),이동규(Dongkyu Lee),김창원(Changwon Kim),주수빈(Subin Joo),권오원(Ohwon Kwon),김민영(Min Young Kim),서준호(Joonho Seo) 제어로봇시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.5
Robotic prosthetic hands are a device that helps to improve the quality of life for patients without hands. Recently, robotic prosthetic hands can perform various grasping patterns because of improvement of bioengineering and robotics. The research that automatically selects the appropriate operation according to the situation is important. Many previous studies have used EMG signals. However, EMG signals are difficult to generalize because EMG signals vary depending on the position of the muscle. In this study, we developed a system for controlling robotic prosthetic hands using images and deep learning to facilitate generalization. We also proposed a method for selecting a grasping target to be held in the image. These results will help to improve the quality of life of the robotic prosthetic hand user.