• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.97-104
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• 2012

Natural User Interface(NUI) technologies introduce new trends in using devices such as computer and any other electronic devices. In this paper, an augmented reality on a mobile device is implemented by using face detection, recognition and motion tracking. The face detection is obtained by using Viola-Jones algorithm from the images of the front camera. The Eigenface algorithm is employed for face recognition and face motion tracking. The augmented reality is implemented by overlapping the rear camera image and GPS, accelerator sensors' data with the 3D graphic object which is correspond with the recognized face. The algorithms and methods are limited by the mobile device specification such as processing ability and main memory capacity.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.223-228
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• 2011

In this paper, we proposed a technique to recognize three states in stance phase of gait cycle. Walking assistive devices are used to help the elderly people walk or to monitor walking behavior of the disabled persons. For the effective assistance, they adopt an intelligent sensor system to understand user's current state in walking. There are three states in stance phase; Loading Response, Midstance, and Terminal Stance. We developed a foot pressure sensor using 24 FSRs (Force Sensing/Sensitive Resistors). The foot pressure patterns were integrated through the interpolation of FSR cell array. The pressure patterns were processed to get the trajectories of COM (Center of Mass). Using the trajectories of COM of foot pressure, we can recognize the three states of stance phase. The experimental results show the effective recognition of stance phase and the possibility of usage on the walking assistive device for better control and/or foot pressure monitoring.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.53-57
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• 2009

In this paper to computerized patient management of patients applying for a facial recognition algorithm to extract Face Feature Points environment, the implementation of the U-Healthcare offers. First, mobile devices and the pictures and photos of the patient data used as input data, the algorithm AdaBoost Face Feature Points patterns extracted, then stored in an existing database, extracted from the patient's sample photos, matching patterns and makes Face Feature Points. The result is the same patient if the patient information database, in recognizing the disease, doctors, and medical fields to extract the relevant information on the screen to output devices, the patient will present the implementation of recognition system.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.3
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• pp.135-138
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• 2014

This paper devices a facial expression recognition method robust to overfitting using 2D-DCT and EHMM algorithm. In particular, this paper achieves enhanced recognition performance by setting up a large window size for 2D-DCT feature extraction and extracting the observation vectors of EHMM. The experimental results on the CK facial expression database and the JAFFE facial expression database showed that the facial expression recognition accuracy was improved according as window size is large. Also, the proposed method revealed the recognition accuracy of 87.79% and showed enhanced recognition performance ranging from 46.01% to 50.05% in comparison to previous approaches based on histogram feature, when CK database is employed for training and JAFFE database is used to test the recognition accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.457-461
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• 2017

Barcode is widely being used in many places such as supermarket, cafeteria, library, etc. ISBN, Code 128, Code 39 are mainly used in barcode. Among them, Code 128 which is based on ASCII Code can transfer control letters that range from ASCII Code 0 to ASCII 32. Control letters intrinsically imply letters that are used to deliver information to peripheral devices such as a printer or communication joint, however, they play quite different roles if they are inputted on Windows. Generally, barcode devices doesn't verify input data, thus it enables people to tag any barcode that has specific control letters and execute the commands. Besides, most barcode recognition programs are using a database and they have more security weakness compared to other programs. On the basis of those reasons, I give an opinion that SQL Injection can attack barcode recognition programs through this study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1349-1354
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• 2017

In this paper, we propose a fast hand-gesture recognition algorithm for embedded system. Existing hand-gesture recognition algorithm has a difficulty to use in a low performance system such as embedded systems and mobile devices because of high computational complexity of contour tracing method that extracts all points of hand contour. Instead of using algorithms based on contour tracing, the proposed algorithm uses concentric-circle tracing method to estimate the abstracted contour of fingers, then classify hand-gestures by extracting features. The proposed algorithm has an average recognition rate of 95% and an average execution time of 1.29ms, which shows a maximum performance improvement of 44% compared with algorithm using the existing contour tracing method. It is confirmed that the algorithm can be used in a low performance system such as embedded systems and mobile devices.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.398-405
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• 2018

In the HCI, hand gesture recognition is attracting attention as a method for interaction and information exchange between users and devices along with the development of IT devices. In hand gesture recognition through image processing, palm region detection is a key process contributing to improvement of processing speed and recognition rate. In this paper, we propose a new method for image segmentation between the hand and wrist for palm area detection. The anatomical characteristics of the hand are used to calculate the distance between the iliac bones of the thumb and little finger, which have the widest width, by the horizontal projection histogram of the hand image, and then the palm area is detected by drawing a circle having the width as the diameter. In order to verify the superiority of this method, multiple stage template matching is used to compare and evaluate recognition performance against the four conventional methods for 10 hand gestures. Note that the literatures to offer palm area detection performance evaluation are few although there are many studies on hand gesture recognition.

• Journal of Korea Game Society
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• v.9 no.5
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• pp.63-72
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• 2009

I-phone presented a new mobile control type by the introducing motion recognition sensor and that influenced on game application development so that led a lot of games using kinds of sensors. In this paper, we divided the game enjoyment into 5 factors for the embodiment of the sensor's influence direction on the enjoyment. We experimented two game, which have same content, different devices in using motion recognition sensor or not for clarifying the distinction between devices. As a experiment source game, we used Cooking Mama, as a experimental device, we used I-pod touch and NDS. This experiment shows a motion recognition sensor control's enjoyment is far superior to touch sensor. This sensor got high marks on every fun factors, stimulus, absorption, empathy, accomplishment and variation. Especially, stimulus and empathy showed great differences. In this case, we found the fact that extended communication between the gamer and the device can be fun.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.667-677
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• 2022

The purpose of this study is to analyze the structural relationship among the usefulness, ease of use, use intention, and learning utilization of smart learning devices recognized by college students. In order to achieve this, the following problems were addressed: first, how do college students' recognition of ease and usefulness of smart learning devices? second, how do college students' recognition of ease and learning utilization? third, what mediating effects od use of smart learning devices and learning utilization? As for the research method, a survey of 350 students who participated in smart learning was conducted to conduct a home review, confirmatory factor analysis, and bootsrapping for structural equation estimation and mediating analysis. As a result of the analysis on this, first, it was found that the usefulness and ease of smart learning devices recognized by college students had an effect on the intention of use. Second, it was found that the perception of the usefulness and ease of smart devices perceived by college students had an effect on the use of smart learning device learning. Third, it was found that the intention to use smart devices perceived by college students mediated the relationship between usefulness and learning utilization, and it was found that it mediated the relationship between ease and learning utilization. The implications are that instructors can recognize and utilize the intention of using smart learning devices properly by allowing college students to recognize the usefulness and ease of using smart learning devices in the classroom, thereby increasing the learning utilization of smart learning devices in class. In addition, efforts are needed to enable college students to recognize the usefulness of smart devices and to expand the use of learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.591-599
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• 2020

Deep learning is widely utilized to classify or recognize objects of real-world. An abundance of data is trained on high-performance computers and a trained model is generated, and then the model is loaded in an inferencer. The inferencer is used in various environments, so that it may cause unrecognized objects or low-accuracy objects. To solve this problem, real-world objects are collected and they are trained periodically. However, not only is it difficult to immediately improve the recognition rate, but is not easy to learn an inferencer on embedded devices. We propose a real-time handwriting recognizer based on partial learning on embedded devices. The recognizer provides a training environment which partially learn on embedded devices at every user request, and its trained model is updated in real time. As this can improve intelligence of the recognizer automatically, recognition rate of unrecognized handwriting increases. We experimentally prove that learning and reasoning are possible for 22 numbers and letters on RK3399 devices.