• International Journal of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.71-76
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• 2018

The technology convergence of television, communication, and computing devices enables the rich social and entertaining experience through Smart TV in personal living space. The powerful smart TV computing platform allows to provide various user interaction interfaces like IR remote control, web based control, body gesture based control, etc. The presently used smart TV interaction user control methods are not efficient and user-friendly to access different type of media content and services and strongly required advanced way to control and access to the smart TV with easy user interface. This paper propose the optical wireless communication (OWC) based remote controller design for Smart TV using smart device Flashlights. In this approach, the user smart device act as a remote controller with touch based interactive smart device application and transfer the user control interface data to smart TV trough Flashlight using visible light communication method. The smart TV built-in camera follows the optical camera communication (OCC) principle to decode data and control smart TV user access functions according. This proposed method is not harmful as radio frequency (RF) radiation does it on human health and very simple to use as well user does need to any gesture moves to control the smart TV.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1348-1353
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• 2006

본 논문은 3 차원 게임 제어를 위한 인간의 동작인식에 대하여 기술하고 있다. 사용자의 편의성을 위하여 게임에 직관적으로 적용할 수 있는 인간의 동작들을 마커프리 모션 캡쳐 장비를 이용하여 취득하고, 이를 실시간으로 인식하는 동작 인식 시스템을 개발하였다. 또한, 개발된 동작인식기를 이용한 3차원 게임으로의 응용시스템의 접근 방식에 대하여 기술하고 있다. 개발된 동작 인식기는 LDA 방식에 기반을 둔 확률적 접근 방식으로 실시간으로 빠르고 정확하게 응용 시스템에 필요한 인간의 동작을 구별할 수 있도록 설계되었다. 개발된 시스템에서는 인식된 결과를 실시간으로 실제 어플리케이션에 전달하여 그 결과를 직접 사용자가 판단하여 다음 동작을 수행 할 수 있도록 되어 있다. 본 논문은, 이러한 실제 시스템의 개발을 통하여, 3 차원 인간 동작의 간단하고 유용한 활용 방법에 대한 해법을 제시하고 있다.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.991-995
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• 2005

Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as health, environment and habitat monitoring, military, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modern teaming techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes haying the capability of simple processing and wireless communication. The proposed system is able to perform classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to hand gesture recognition system.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.401-407
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• 2015

In this paper, we propose the method to eliminate EMI (Electro-Magnetic Interference) in ELF (Extremely Low Frequency) band below 2 KHz for extending the gesture-recognition distance of smart TVs to more than 3m using electric potential sensor. First, we measure the electric field generated from the back panel of a TV and propose the effective arrangement of two sets of differential sensors as well as the shielding method using metal fiber. Also, we eliminate the PLN (Power Line Noise) and other noise generated from the TV and sensors as well as surrounding environments using filters. Using the proposed EMI eliminating methods, we evaluate displacement ratio on measured signals according to distance between sensors and a moving hand. Experiment results show that our proposed method can extend the hand-gesture sensing distance using EPS (Electric Potential Sensor) up to more than 3m, which is enough to satisfy applicability of EPS based remote control to Smart TVs.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.175-178
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• 2015

This paper presents a VR-simulated sailor training platform for emergency in order to prevent a human error that causes 60~80% of domestic/ abroad marine accidents. Through virtual reality technology, the proposed platform provides an interaction method for proficiency of procedures in emergency, and a crowd control method for controlling crowd agents in a virtual ship environment. The interaction method uses speech recognition and gesture recognition to enhance the immersiveness and efficiency of the training. The crowd control method provides natural simulations of crowd agents by applying a behavior model that reflects the social behavior model of human. To examine the efficiency of the proposed platform, a prototype whose virtual training scenario describes the outbreak of fire in a ship was implemented as a standalone system.

• Smart Media Journal
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• v.8 no.1
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• pp.51-58
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• 2019

This paper describes a gesture application to controlling the special effects of physical devices for 4D contents using the PWM (Pulse Width Modulation) method. The user operation recognized by the infrared sensor is interpreted as a command for 3D content control, several of which manipulate the device that generates the special effect to display the physical stimulus to the user. With the content controlled under the NUI (Natural User Interface) technique, the user can be directly put into an immersion experience, which leads to provision of the higher degree of interest and attention. In order to measure the efficiency of the proposed method, we implemented a PWM-based real-time linear control system that manages the parameters of the motion recognition and animation controller using the infrared sensor and transmits the event.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.1 s.307
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• pp.17-26
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• 2006

This paper proposes a novel hidden Markov model (HMM)-based gesture recognition method and applies it to an HCI to control a computer game. The novelty of the proposed method is two-fold: 1) the proposed method uses a continuous streaming of human motion as the input to the HMM instead of isolated data sequences or pre-segmented sequences of data and 2) the gesture segmentation and recognition are performed simultaneously. The proposed method consists of a single HMM composed of thirteen gesture-specific HMMs that independently recognize certain gestures. It takes a continuous stream of pose symbols as an input, where a pose is composed of coordinates that indicate the face, left hand, and right hand. Whenever a new input Pose arrives, the HMM continuously updates its state probabilities, then recognizes a gesture if the probability of a distinctive state exceeds a predefined threshold. To assess the validity of the proposed method, it was applied to a real game, Quake II, and the results demonstrated that the proposed HMM could provide very useful information to enhance the discrimination between different classes and reduce the computational cost.

• Journal of Platform Technology
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• v.11 no.1
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• pp.38-51
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• 2023

We propose an intelligent interface algorithm using hand gesture recognition information based on artificial intelligence. This method is functionally an interface that recognizes various motions quickly and intelligently by using MediaPipe and artificial intelligence techniques such as KNN, LSTM, and CNN to track and recognize user hand gestures. To evaluate the performance of the proposed algorithm, it is applied to a self-made 2D top-view racing game and robot control. As a result of applying the algorithm, it was possible to control various movements of the virtual object in the game in detail and robustly. And the result of applying the algorithm to the robot control in the real world, it was possible to control movement, stop, left turn, and right turn. In addition, by controlling the main character of the game and the robot in the real world at the same time, the optimized motion was implemented as an intelligent interface for controlling the coexistence space of virtual and real world. The proposed algorithm enables sophisticated control according to natural and intuitive characteristics using the body and fine movement recognition of fingers, and has the advantage of being skilled in a short period of time, so it can be used as basic data for developing intelligent user interfaces.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1319-1323
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• 2004

In this paper, a problem of Thai sign language recognition using a neural network is considered. The paper addresses the problem in classifying certain signs conveying quantitative meaning, e.g., large or small. By treating those signs corresponding to different quantities as derived from different classes, the recognition error rate of the standard multi-layer Perceptron increases if the precision in recognizing different quantities is increased. This is due the fact that, to increase the quantitative recognition precision of those signs, the number of (increasingly similar) classes must also be increased. This leads to an increase in false classification. The problem is due to misinterpreting the amount of quantity the quantitative signs convey. In this paper, instead of treating those signs conveying quantitative attribute of the same quantity type (such as 'size' or 'amount') as derived from different classes, here they are considered instances of the same class. Those signs of the same quantity type are then further divided into different subclasses according to the level of quantity each sign is associated with. By using this two-level classification, false classification among main gesture classes is made independent to the level of precision needed in recognizing different quantitative levels. Moreover, precision of quantitative level classification can be made higher during the recognition phase, as compared to that used in the training phase. A standard multi-layer Perceptron with a back propagation learning algorithm was adapted in the study to implement this two-level classification of quantitative gesture signs. Experimental results obtained using an electronic glove measurement of hand postures are included.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.343-352
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• 2012

In this paper, we implemented a touchless interface for DID(Digital Information Display) system using gesture recognition technique which includes both hand motion and hand shape recognition. Especially this touchless interface without extra attachments gives user both easier usage and spatial convenience. For hand motion recognition, two hand-motion's parameters such as a slope and a velocity were measured as a direction-based recognition way. And extraction of hand area image utilizing YCbCr color model and several image processing methods were adopted to recognize a hand shape recognition. These recognition methods are combined to generate various commands, such as, next-page, previous-page, screen-up, screen-down and mouse -click in oder to control DID system. Finally, experimental results showed the performance of 93% command recognition rate which is enough to confirm the possible application to commercial products.