• Journal of Biomedical Engineering Research
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• v.35 no.2
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• pp.14-18
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• 2014

This study is about e-Book program based on human-computer interaction(HCI) system for physically handicapped person. By acquiring background knowledge of HCI, we know that if we use vision-based interface we can replace current computer input devices by extracting any characteristic point and tracing it. We decided betweeneyes as a characteristic point by analyzing facial input image using webcam. But because of three-dimensional structure of glasses, the person who is wearing glasses wasn't suitable for tracing between-eyes. So we changed characteristic point to the bridge of the nose after detecting between-eyes. By using this technique, we could trace rotation of head in real-time regardless of glasses. To test this program's usefulness, we conducted an experiment to analyze the test result on actual application. Consequently, we got 96.5% rate of success for controlling e-Book under proper condition by analyzing the test result of 20 subjects.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.368-374
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• 2011

This paper proposes a robot control human interface using Markov model (HMM) based hand signal recognizer. The command receiving humanoid robot sends webcam images to a client computer. The client computer then extracts the intended commanding hum n's hand motion descriptors. Upon the feature acquisition, the hand signal recognizer carries out the recognition procedure. The recognition result is then sent back to the robot for responsive actions. The system performance is evaluated by measuring the recognition of '48 hand signal set' which is created randomly using fundamental hand motion set. For isolated motion recognition, '48 hand signal set' shows 97.07% recognition rate while the 'baseline hand signal set' shows 92.4%. This result validates the proposed hand signal recognizer is indeed highly discernable. For the '48 hand signal set' connected motions, it shows 97.37% recognition rate. The relevant experiments demonstrate that the proposed system is promising for real world human-robot interface application.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.17 no.3
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• pp.233-240
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• 2013

Objectives The objective and universal grading system for the facial nerve palsy is needed to the objectification of treatment in Oriental medicine. In this study, the facial nerve palsy grading was developed with combination of image processing technique and Nottingham scale. Methods The developed system is composed of measurement part, image processing part, facial nerve palsy evaluation part, and display part. With the video data recorded by webcam at measurement part, the positions of marker were measured at image processing part. In evaluation part, Nottingham scales were calculated in four different facial expressions with measured marker position. The video of facial movement, time history of marker position, and Nottingham scale were displayed in display part. Results & Conclusion The developed system was applied to a normal subject and a abnormal subject with facial nerve palsy. The left-right difference of Nottingham scores was large in the abnormal compared with the normal. In normal case, the change of the length between supraorbital point and infraorbital point was larger than that of the length between lateral canthus and angle of mouth. The abnormal case showed an opposite result. The developed system showed the possibilities of the objective and universal grading system for the facial nerve palsy.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.399-408
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• 2012

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.97-103
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• 2020

The proposed system is composed of two parts, an AI physical fitness measurement part and an AI physical fitness management part. In the AI fitness measurement part, a guide to physical fitness measurement and accurate calculation of the measured value are performed through deep learning-based pose recognition. Based on these measurements, the AI fitness management part designs personalized exercise programs and provides them to dedicated smart applications. To guide the measurement posture, the posture of the subject to be measured is photographed through a webcam and the skeleton line is extracted. Next, the skeletal line of the learned preparation posture is compared with the extracted skeletal line to determine whether or not it is normal, and voice guidance is provided to maintain the normal posture.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.68-73
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• 2022

In this study, we produced the surface defect detection model to automatically detect defect bone plates using a deep learning algorithm. Bone plates with a width and a length of 50 mm are most used for fracture treatment. Normal bone plates and defective bone plates were printed on the 3d printer. Normal bone plates and defective bone plates were photographed with 1,080 pixels using the webcam. The total quantity of collected images was 500. 300 images were used to learn the defect detection model. 200 images were used to test the defect detection model. The mAP(Mean Average Precision) method was used to evaluate the performance of the surface defect detection model. As the result of confirming the performance of the surface defect detection model, the detection accuracy was 96.3 %.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.8 no.1
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• pp.3-8
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• 2022

Alteration of the mGluR5 density is closely related to various brain diseases including schizophrenia, depression, Parkinson's disease, and Alzheimer's disease. Therefore, mGluR5 is considered as a valuable imaging biomarker for brain disease and many radiopharmaceuticals have been developed so far. Among them, [¹⁸F]FPEB has favorable pharmacokinetic characteristics, and this is the most frequently used radiopharmaceutical for preclinical and clinical studies. In the present study, we want to introduce the optimized radiosynthetic method for the routine production of [¹⁸F]FPEB using a GE TRACERlabTM FXFN pro module. In addition, the entire process was monitored with a webcam to solve the problems arising from the synthetic process. As a result, [¹⁸F]FPEB was prepared by nucleophilic substitution from its nitro- precursor at 120℃ for 20 min in dimethyl sulfoxide. Radiochemical yield was 13.7 ± 5.1% (decay-corrected, n = 91) with the molar activity of 84 ± 17 GBq/µmol at the end of synthesis. The radiochemical purity was determined to be above 96%. The manufactured [¹⁸F]FPEB injection for quality controls were carried out in accordance with an KIRAMS approved protocol, as per ICH and USP guidelines.

• Physical Therapy Korea
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• v.29 no.2
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• pp.87-93
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• 2022

To solve the pathological problems of the musculoskeletal system based on evidence, a sophisticated analysis of human motion is required. Traditional optical motion capture systems with high validity and reliability have been utilized in clinical practice for a long time. However, expensive equipment and professional technicians are required to construct optical motion capture systems, hence they are used at a limited capacity in clinical settings despite their advantages. The development of information technology has overcome the existing limit and paved the way for constructing a motion capture system that can be operated at a low cost. Recently, with the development of computer vision-based technology and optical markerless tracking technology, webcam-based 3D human motion analysis has become possible, in which the intuitive interface increases the user-friendliness to non-specialists. In addition, unlike conventional optical motion capture, with this approach, it is possible to analyze motions of multiple people at simultaneously. In a non-optical motion capture system, an inertial measurement unit is typically used, which is not significantly different from a conventional optical motion capture system in terms of its validity and reliability. With the development of markerless technology and advent of non-optical motion capture systems, it is a great advantage that human motion analysis is no longer limited to laboratories.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.382-384
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• 2021

In this study, we implement a system that automatically classifies types of plastics using YOLO (You Only Look Once), a real-time object recognition algorithm. The system consists of Nvidia jetson nano, a small computer for deep learning and computer vision, with model trained to recognize plastic separation emission marks using YOLO. Using a webcam, recycling marks of plastic waste were recognized as PET, HDPE, and PP, and motors were adjusted to be classified according to the type. By implementing this automatic classifier, it is convenient in that it can reduce the labor of separating and discharging plastic separation marks by humans and increase the efficiency of recycling through accurate recycling.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.33-38
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• 2016

DDoS attack has been continuously utilized that caused the excessively large amount of traffic that network bandwidth or server was unable to deal with paralyzing the service. Most of the people regard NTP as the biggest cause of DDoS. However, according to recently executed DDoS attack, there have been many SSDP attack in the use of amplified technique. According to characteristics of SSDP, there is no connection for making a forgery of source IP address and amplified resources feasible. Therefore, it is frequently used for attack. Especially, as it is mostly used as a protocol for causing DDoS attack on IoT devices that constitute smart home including a wireless router, media server, webcam, smart TV, and network printer. Hereupon, it is anticipated for servers of attacks to gradually increase. This might cause a serious threat to major information of human lives, major government bodies, and company system as well as on IoT devices. This study is intended to identify DDoS attack techniques in the use of weakness of SSDP protocol occurring in IoT devices and attacking scenario and counter-measures on them.