• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.121-123
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• 2009

뱀형 모듈라 로봇은 다양한 환경에 대해서 강인성을 가지고 있고, 모듈 일부의 고장에도 이동할 수 있는 장점을 가진다. 그러나 이동 제어 방법이 어렵고, 아직까지 효율적인 이동법의 개발이 미비한 편이다. 본 연구에서는 뱀형 로봇의 이동제어를 위하여 GA(Genetic Algorithm)기반의 위상제어 방식과 GP(Genetic Programing)를 사용한 임의의 관절궤적 생성 방식을 비교한다. KMC사의 뱀형 로봇을 대상으로 먼저 webots 시뮬레이터 상에서 모델링 및 시뮬레이션을 수행하여 위의 방법들을 비교하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.847-848
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• 2013

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.941-948
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• 2022

Disaster response robots are deployed to disaster sites where human access is difficult and dangerous. The disaster response robots explore the disaster sites prevent a structural collapse and perform lifesaving to minimize damage. It is difficult to operate robots in the disaster sites due to rough terrains where various obstacles are scattered, communication failures and invisible environments. In this paper, we developed a series connectable wheeled robot module. The series connectable wheeled robot module was developed into two types: an active driven robot module and a passive driven robot module. A wheeled robot was built by connecting the two active type robot modules and one passive type robot module. Two robot modules were connected by one DoF rotating joint, allowing the wheeled robot to avoid obstructions in a vertical direction. The wheeled robot performed driving and obstacle avoidance using only pressure sensors, which allows the wheeled robot operate in the invisible environment. An obstacle avoidance experiment was conducted to evaluate the performance of the wheeled robot consisting of two active driven wheeled robot modules and one passive driven wheeled robot module. The wheeled robot successfully avoided step-shaped obstacles with a maximum height of 80 mm in a time of 24.5 seconds using only a pressure sensors, which confirms that the wheeled robot possible to perform the driving and the obstacle avoidance in invisible environment.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.2
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• pp.137-144
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• 2015

In this paper, we present an inertial sensor-based gait analysis system to measure and analyze lower-limb movements. We developed an integral AHRS(Attitude Heading Reference System) using a combination of rate gyroscope, accelerometer and magnetometer sensor signals. Several AHRS modules mounted on segments of the patient's body provide the quaternions representing the patient segments's orientation in space. And a method is also proposed for calculating three-dimensional inter-segment joint angle which is an important bio-mechanical measure for a variety of applications related to rehabilitation. To evaluate the performance of our AHRS module, the Vicon motion capture system, which offers millimeter resolution of 3D spatial displacements and orientations, is used as a reference. The evaluation resulted in a RMSE(Root Mean Square Error) of 1.08 and 1.72 degree in yaw and pitch angle. In order to evaluate the performance of our the gait analysis system, we compared the joint angle for the hip, knee and ankle with those provided by Vicon system. The result shows that our system will provide an in-depth insight into the effectiveness, appropriate level of care, and feedback of the rehabilitation process by performing real-time limb or gait analysis during the post-stroke recovery.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.6
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• pp.585-592
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• 2014

In this study, a multibody simulator was developed to analyze the bio-mimetic motion of a lizard robot design. A RecurDyn multibody dynamics model of a lizard was created using a micro-computerized tomography scan and motion capture data. The bio-mimetic motion simulator consisted of a trajectory generator, an inverse kinematics module, and an inverse dynamics module, which were used for various walking motion analyses of the developed lizard model. The trajectory generation module produces spinal movements and gait trajectories based on the lizard's speed. Using the joint angle history from an inverse kinematic analysis, an inverse dynamic analysis can be carried out, and the required joint torques can be obtained for the lizard robot design. In order to investigate the effectiveness of the developed simulator, the required joint torques of the model were calculated using the simulator.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.873-876
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• 2004

사람의 동작을 인식하는 것에 대한 연구는 게임, 유비쿼터스 컴퓨팅 등의 발전에 따라 그 중요성이 증가하고 있다. 그러나, 대부분의 기존 연구에서는 극히 소수의 동작만을 정의하거나 특정 부위의 동작만을 다루므로 실제 응용에 적용하기에는 적합하지 않다. 본 논문에서는 특정 도메인의 사용 없이, 카메라 영상 입력으로 취득된 동작 패턴 정보만을 이용하여 40종 전신 연속 동작을 구분하는 동작인식 방법을 연구하였다. 인식에 사용된 입력 데이터는 동작자 관절들의 위치 및 회전 값들이며, 다수의 동작들을 인식하기 위해서는 기존의 인식 알고리즘들인 특징기반 인식, HMM, 신경망(Neural Network)등을 사용하여 복합적인 인식 엔진을 구성하여야 했다. 입력 데이터별로 적합한 인식 모듈을 거치게 하기 위해서는, 동작에 의한 입력 데이터에서 동작자 움직임의 주요 신체 부위를 추출함으로써 입력 데이터가 해당 그룹의 인식 모듈로 자동적으로 분류되게 하는 방법을 사용한다. 이는 다층의 인식 레이어 중 복잡도가 증가하는 하위 레이어일수록 자동 분류에 의해 걸러진 데이터만을 취급하게 되므로 효과적이다. 전체 실험 결과 단계별로 약 79~97%의 인식률을 보였다. 이는 향후 특정 컨텍스트 정보와 결합할 때 매우 높은 인식률을 기대할 수 있게 하는 수치이다.

• Science of Emotion and Sensibility
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• v.21 no.3
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• pp.165-176
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• 2018

The objective of this study was to develop clothing-type wearable motion sensing and feedback systems to enhance children's sports by promoting visual and audio feedback. In this study, several applications, such as fabric sensors, sportswear integrated with various types of fabric sensors, and fabric-based motion sensing module design, as well as a visual and audio feedback system for gaining a better understanding of a child's interest in a type of exercise, were developed. An SWCNT-based stretchable fabric sensor was developed for motion sensing, and sportswear was designed using the fabric sensor that was integrated into the limbs of the garment. The sensing module was developed, and sensory performance was evaluated through a joint motion experiment for children. In addition, using the feedback system that was developed in the form of an accessory, the responses of light and sound were also examined based on the movement of the child who was wearing the sportswear prototypes. This study focused on the development and assessment of prototype designs for children's sportswear and accessory products that can help to ascertain a child's interest in a particular exercise.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.403-408
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• 2015

Kinect sensors have the ability to recognize the behavior and voice of the user. Due to its low-cost and high accessibility, Kinect sensors have been used in various fields, including healthcare, education and so on. In this paper, we propose to use Kinect in piano education. Specifically, the proposed method first recognizes the coordinate values of user's posture, compares them with coordinate values of teacher's posture and provide real-time feedbacks to the user. This enables user to keep the correct posture even when he is learning piano without a teacher. However, since the piano education is a long process, it is difficult to achieve the correct posture as a teacher immediately. Thus, we propose a user-oriented method to measure the error tolerance rate. The proposed method is the first feedback based piano education system that uses Kinect sensors.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.11
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• pp.1243-1248
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• 2011

The purposes of this study were to develop a dynamic model of a human and to investigate the effect of a walker on an elderly subject's motions, such as sit-to-stand (STS) motion and normal gait, by using this model. A human model consisting of 15 segments and 14 joints was developed, embedded in $RecurDyn^{TM}$, and connected through a Simulink$^{(R)}$ interface with collected motion data. The model was validated by comparisons between joint kinematic results from inverse dynamics (Matlab$^{(R)}$-based in-house program) and from $RecurDyn^{TM}$ simulation during walking. The results indicate that the elderly walker induced a longer movement time in walking, such that the speed of joint flexion/extension was slower than that during a normal gait. The results showed that the muscle activities of parts of the ankle and hamstring were altered by use of the elderly walker. The technique used in this study could be very helpful in applications to biomechanical fields.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.155-161
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• 2021

Gesture recognition analytics through a camera in real time have been widely studied in recent years. Since a small number of features from human joints are extracted, low accuracy of classifying models is get in conventional gesture recognition studies. In this paper, CBAM (Convolutional Block Attention Module) with high accuracy for classifying images is proposed as a classification model and algorithm calculating the angle of joints depending on actions is presented to solve the issues. Employing five exercise gestures images from the fitness posture images provided by AI Hub, the images are applied to the classification model. Important 8-joint angles information for classifying the exercise gestures is extracted from the images by using MediaPipe, a graph-based framework provided by Google. Setting the features as input of the classification model, the classification model is learned. From the simulation results, it is confirmed that the exercise gestures are classified with high accuracy in the proposed model.