• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.14-18
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• 2017

In this paper, an contemplate the direction for Usable Security in IT security and User Experience. To evaluate how the user interface is convenient to use, we examine the components such as the property, learnable property, memory simplicity, faults and satisfaction level. By considering for the security, we should bring positive effects on the user experience. By emphasizing usability and security at the same time, we should increase the satisfaction level of the user experience and then produce the valuable experience through participation, use and observation. The positive user experience is the important task for the software engineering, business administration and others., and this will result satisfaction of the users, brand trust, and success in the market. On the other hand, for the negative user experience, the users cannot achieve their desired goal and therefore, are unsatisfied due to emotional, rational and economic inconvenience. Due to this, we should try to maintain a certain level of usability and security of the system in IT security and User Experience.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1138-1156
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• 2021

Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2032-2038
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• 2006

The recognition of human gestures in image sequences is an important and challenging problem that enables a host of human-computer interaction applications. This paper describes a gesture recognition algorithm based on the particle filters, namely CONDENSATION. The particle filter is more efficient than any other tracking algorithm because the tracking mechanism follows Bayesian estimation rule of conditional probability propagation. We used two models for the evaluation of particle Inter and apply the MATLAB for the preprocessing of the image sequence. But we implement the particle filter using the C++ to get the high speed processing. In the experimental results, it is demonstrated that the proposed algorithm prove to be robust in the cluttered environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.584-591
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• 2007

The recognition of human gestures in image sequences is an important and challenging problem that enables a host of human-computer interaction applications. This paper describes a gesture recognition algorithm based on the particle filters, namely CONDENSATION. The particle filter is more efficient than any other tracking algorithm because the tracking mechanism follows Bayesian estimation rule of conditional probability propagation. We used two models for the evaluation of particle filter and apply the MAILAB for the preprocessing of the image sequence. But we implement the particle filter using the C++ to get the high speed processing. In the experimental results, it is demonstrated that the proposed algorithm prove to be robust in the cluttered environment.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2397-2401
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• 2005

It is well understood that force reflecting coupled with visual display can be an important two-way communication channel in human-computer interaction. In this work, important components for a high-fidelity system bandwidth are force reflecting device and that all the computations including contact determination and response computation have to be performed in less than a millisecond. This paper describes a force-reflecting device and an embedded controller. The realized force-reflecting device is based on a novel serial type mechanical structure, and features compactness, high sustained output force capability, low friction, zero backlash, and enough workspace. The embedded controller reduces software computational load via main processor and simplifies hardware strictures by the time-division control. The device is integrated with existing dynamic simulation algorithms running separate workstation, so that objects can be manipulated in real time and the corresponding forces felt back by the operator.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.22-30
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• 2010

3D TV was been able to see in the market in early in this year. Tablet PC such as ipad by Apple and Galaxytab by Samsung were introduced recently. Those are possible by developing H/W and S/W of computer technology. The needs of interactive contents in many areas including education and entertainment area are increasing rapidly according to the various information devices are or will be in the market. Fore the more, GoogleTV and AppleTV are compete each other to dominate the world market in advance recently. CookTV tries to dominate in the domestic market by upgrading the current system. Diverse information devices are in the market means various size of displayers are able to be shown in our life. As TV is fused to computer, the displayer is substituted to TV's screen and the trend of TV is became bigger. The evolved TV is able to replace the computer by connecting to the network and people want to do interactions with contents by using the bidirectional communication. Therefore, it is expected to changing the human lifestyle. It is natural that contents for all members of family are needed, since TV's screen become bigger. It is required that the contents should guarantees the accessability of information to the all of family members and the easy interaction with contents. Our goal of experiment are to analyse the influence of interaction with contents as the size of images and to analyse a learning effect of contents quantitatively by applying a statistical method. Users interacted with contents without any difficulty when they met a same dimension and shape of objects as ame dimension and shape objects in their experiences or learning, was confirmed. And the learning effect were analysed and explained by applying the correlation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1087-1104
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• 2015

An eye location based head posture recognition method is proposed in this paper. First, face is detected using skin color method, and eyebrow and eye areas are located based on gray gradient in face. Next, pupil circles are determined using edge detection circle method. Finally, head postures are recognized based on eye location information. The proposed method has high recognition precision and is robust for facial expressions and different head postures, and can be used in mouse operation. The experimental results reveal the validity of proposed method.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.103-110
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• 2016

Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.

• Journal of Information Technology Applications and Management
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• v.12 no.2
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• pp.145-161
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• 2005

In this paper, we present an experiment conducted to examine how the web site's architecture, content quantity and the user task complexity affect its usability. The experiment was performed in two phases on college students to visit existing web sites which are readily accessible via the Internet. The results of experiment show that the web site's architecture significantly affects the efficiency and the effectiveness interactively with its content quantity and the user task's complexity. On the other hand, none of the above three factors show significant effects on the satisfaction and the learnability. In particular, the web site's content quantity does not have any statistically significant effects on the satisfaction and the learnability. It implies that the factors affecting the satisfaction and the learnability are different from those affecting the efficiency and the effectiveness. The analysis reassures that it is essential to consider the context of use in designing a web site.