Earticle

This paper proposes a method of simulating the movements of the human body by automatically extracting the characteristics of an object from a camera and sending them to mobile devices. In this method, a RGB color video from a camera was converted into hue, saturation, and intensity images for extracting the silhouettes of the human body using digital subtraction of the images. Using the corner points and modeling information from the extracted silhouettes, joints are automatically detected and used as each connection points of the object. Also, block-matching algorithms were applied to extracted joints to track the characteristics, which are sent to mobile devices. On the mobile devices, body movements are simulated using the received joints. The results showed that the method automatically detects silhouettes and joints and effectively simulates the human body using the extracted joints. Also, the tracking of the joints were reflected in the mapped body, allowing for appropriate tracking the movements.

In recent years, much progress has been made in the automation of welding coped with a variety of highly flexible sensors. Among these sensors, only the eddy current sensor can detect the center location of the butt joint whose gap distance is zero. Thus, in this study the eddy current sensor is used to develop a robust and useful joint tracking system. The developed system is tested to qualify the performance of the system and seam tracking algorithm is proposed and two simulation are executed to show the performance of the proposed tracking algorithm.

Welding fabrication invariantly involves three district sequential steps: preparation, actual process execution and post-weld inspection. One of the major problems in automating these steps and developing autonomous welding system is the lack of proper sensing strategies. Conventionally, machine vision is used in robotic arc welding only for the correction of pre-taught welding paths in single pass. However, in this paper, multipass tracking more than single pass tracking is performed by conventional seam tracking algorithm and developed one. And tracking performances of two algorithm are compared in multipass tracking. As the result, tracking performance in multi-pass welding shows superior conventional seam tracking algorithm to developed one.

Welding fabrication invariantly involves three district sequential steps: preparation, actual process execution and post-weld inspection. One of the major problems in automating these steps and developing autonomous welding systems, is the lack of proper sensing strategies. Conventionally, machine vision is used in robotic arc welding only for the correction of pre-taught welding paths in single pass. In this paper, novel presented, developed vision processing techniques are detailed, and their application in welding fabrication is covered. The software for joint tracking system is finally proposed.

본 연구는 최근 증가하는 홈트레이닝과 재활치료 이용자들을 대상으로 정확한 자세 교정과 고가 장비 의존 문제를 해결하기 위한 새로운 자세 인식 시스템을 제안한다. 기존의 센서 기반 장비와 모션 캡처 시스템은 비용이 비싸고 불편한 착용 으로 일반 사용자의 접근성이 낮다는 문제를 해결하고자 한다. 제안하는 시스템은 웹캠과 MediaPipe를 활용하여 작은 비용으로도 효율적인 자세 추적과 실시간 피드백이 가능하며, 관절 랜드마크 분석과 GUI 환경을 통해 사용자의 운동 상황을 직관적으로 제공한다. 또한 기준 동작과 비교 및 색상 피드백을 도입하여 자세 일치 여부를 정확히 판단할 수 있고, 녹화 기능을 통해 사용자의 동작을 저장하여 분석이 가능하다. 본 시스템은 웹캠과 MediaPipe를 활용해 33개 관절을 실시간으로 추적하고 기준 동작과의 차이를 분석하여 정교한 관절 추적이 가능하고 웹캠만으로 작동하는 높은 접근성 그리고 실시간으로 피드백 및 녹화 기능을 통한 편의성의 측면에서 혁신적인 솔루션을 제시하며 미래의 스마트 운동 코칭 및 재활 보조 시스템에 대한 가능성을 보여준다.

This study proposes a novel posture-recognition system designed to improve exercise accuracy and reduce dependence on expensive equipment among the increasing population of home-training and rehabilitation users. Conventional sensor-based devices and motion-capture systems often suffer from high costs and discomfort caused by wearable components, which significantly limits accessibility for general users and home-based exercise environments. To address these limitations, the proposed system utilizes a standard webcam and MediaPipe to enable low-cost yet effective posture tracking and real-time visual feedback without requiring additional hardware. Through joint landmark analysis and an intuitive graphical user interface (GUI), users can clearly understand, monitor, and adjust their exercise performance during training sessions. Additionally, by comparing user movements with reference actions and providing color-based feedback, the system accurately determines posture correctness and supports immediate self-correction. A recording feature also allows users to save their exercise data for post-analysis and long-term performance review. By tracking 33 body joints in real time and analyzing differences from reference motions, the system demonstrates precise joint tracking, high accessibility due to its webcam-only operation, and enhanced usability through real-time feedback and recording capabilities. Overall, this system presents an innovative and practical solution for exercise coaching and rehabilitation support, highlighting the potential for future smart fitness applications, personalized exercise guidance, and assistive therapeutic systems.

Because of its fast and precise welding performance, laser welding is becoming a new excellent welding method. However, the precise focusing and robust seam tracking are required to apply laser welding to the practical fields. In order to laser weld a type of T joint like a circular pipe on a square pipe, which could be met in the three dimensional structure such as an aluminum space frame, a visual sensor system was developed for automation of focusing and seam tracking. The developed sensor system consists of a digital CCD camera, a structured laser, and a vision processor. It is moved and positioned by a 2-axis motorized stage, which is attached to a 6 axis robot manipulator with a laser welding head. After stripe-type structured laser illuminates a target surface, images are captured through the digital CCD camera. From the image, seam error and defocusing error are calculated using image processing algorithms which includes efficient techniques handling continuously changed image patterns. These errors are corrected by the stage off-line during welding or teaching. Laser welding of a circular pipe on a square pipe was successful with the vision tracking system by reducing the path positioning and de focusing errors due to the robot teaching or a geometrical variation of specimens and jig holding.

In this study, a visual sensor system which can detect I-butt weld joint with height variation and includes a seam tracking algorithm was investigated. Three-dimensional position of an object can be acquired by using the method of distance measurement, i.e., an optical trigonometry which results from the spatial relations between the camera, the object and the structured light by a visible laser. Effects of laser intensity and iris number for the image quality as well as object material were investigated for the optical system design. For the image processing, a region of interest is defined from the whole image and a line image of laser is drew by using the gray level difference in the image. From the drew laser line, the weld joint can be recognized in searching the biggest point position calculated from the central difference method. Through a series of welding experiments, a good tracking performance was confirmed under GMA welding.