The objectives of this study were to summarize the curriculum, history, and clinical researches of Chuna in Korea and to ultimately introduce Chuna to Western medicine. Information about the history and insurance coverage of Chuna was collected from Chuna-related institutions and papers. Data on Chuna education in all 12 Korean medicine (KM) colleges in Korea were reconstructed based on previously published papers. All available randomized controlled trials (RCTs) of Chuna in clinical research were searched using seven Korean databases and six KM journals. As a result, during the modern Chuna era, one of the three periods of Chuna, which also include the traditional Chuna era and the suppressed Chuna era, Chuna developed considerably because of a solid Korean academic system, partial insurance coverage, and the establishment of a Chuna association in Korea. All of the KM colleges offered courses on Chuna-related subjects (CRSs); however, the total number of hours dedicated to lectures on CRSs was insufficient to master Chuna completely. Overall, 17 RCTs were reviewed. Of the 14 RCTs of Chuna in musculoskeletal diseases, six reported Chuna was more effective than a control condition, and another six RCTs proposed Chuna had the same effect as a control condition. One of these 14 RCTs made the comparison impossible because of unreported statistical difference; the last RCT reported Chuna was less effective than a control condition. In addition, three RCTs of Chuna in neurological diseases reported Chuna was superior to a control condition. In conclusion, Chuna was not included in the regular curriculum in KM colleges until the modern Chuna era; Chuna became more popular as the result of it being covered by Korean insurance carriers and after the establishment of a Chuna association. Meanwhile, the currently available evidence is insufficient to characterize the effectiveness of Chuna in musculoskeletal and neurological diseases.
In the vision-based remote gaze tracking systems, the most challenging topics are to allow natural movement of a user and to increase the working volume and distance of the system. Several eye gaze estimation methods considering the natural movement of a user have been proposed. However, their working volume and distance are narrow and close. In this paper, we propose a novel 2-D mapping-based gaze estimation method that allows large-movement of user. Conventional 2-D mapping-based methods utilize mapping function between calibration points on the screen and pupil center corneal reflection (PCCR) vectors obtained in user calibration step. However, PCCR vectors and their associated mapping function are only valid at or near to the position where the user calibration is performed. The proposed movement mapping function, complementing the user's movement, estimates scale factors between two PCCR vector sets: one obtained at the user calibration position and another obtained at the new user position. The proposed system targets a longer range gaze tracking which operates from 1.4 to 3 m. A narrow-view camera mounted on a pan and tilt unit is used by the proposed system to capture high-resolution eye image, providing a wide and long working volume of about 100 cm × 40 cm × 100 cm. The experimental results show that the proposed method successfully compensated the poor performance due to user's large movement. Average angular error was 0.8° and only 0.07° of angular error was increased while the user moved around 81 cm.
A lane detection system using around view monitoring (AVM) images is presented in this paper. To provide safe driving condition, many lane detection approaches have been proposed. However, previous approaches cannot detect lane stably in low visibility condition such as foggy or rainy days because of the use of frontal camera. The proposed lane detection system uses ego-vehicle's surrounding road information to overcome this problem. The proposed method can be split into two stages: generation of AVM images from four fisheye cameras and lane detection using AVM images. To generate AVM images, we use four fisheye cameras mounted on sides, front, and rear of the vehicle. Top-view images covering the surround area of the vehicle are generated from four fisheye images by calibrations of each camera and their relative camera pose. The lane detection procedure consists of detecting and grouping lane responses, fitting lane responses by a linear model, and tracking lanes with kalman filter to smooth the estimates. Experimental results on full lanes and dashed lanes show that the proposed method can achieve the detection accuracies of 98.78% and 90.88% respectively and processing speed of 1 ms per frame in a desktop computer.
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