• 한국인터넷방송통신학회논문지
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• 제17권5호
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• pp.143-150
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• 2017

본 논문에서는 스마트 폰 추적 및 색상 통신을 이용한 새로운 동작인식 플랫폼을 개발한다. 카메라가 탑재된 PC 혹은 스마트 TV와 개인 스마트 폰 가지고 영상을 기반으로 한 객체 인식 기술을 이용하여 동작 인식 유저 인터페이스를 제공한다. 사용자는 손으로 스마트 폰을 움직여 모션 컨트롤러처럼 사용할 수 있으며, 플랫폼에서는 이 스마트폰을 실시간으로 검출하고, 3차원 거리와 각도를 추정하여 사용자의 동작을 인식한다. 또한, 스마트 폰과 서버의 통신을 위하여 색상 디지털 코드를 이용한 통신 시스템이 사용된다. 사용자들은 색상 통신 방법을 이용하여 텍스트 데이터를 자유자재로 주고받을 수 있으며, 동작을 취하는 도중에도 끊임없이 데이터를 전송할 수 있다. 제안한 동작인식 플랫폼 기반의 실행 가능한 콘텐츠를 구현하여 결과를 제시한다.

• Journal of Platform Technology
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• 제6권1호
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• pp.3-8
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• 2018

This paper proposes a technique for recognizing online handwritten cursive data obtained by tracing a motion trajectory while a user is in the 3D space based on a convolution neural network (CNN) algorithm. There is a difficulty in recognizing the virtual character input by the user in the 3D space because it includes both the character stroke and the movement stroke. In this paper, we divide syllable into consonant and vowel units by using labeling technique in addition to the result of localizing letter stroke and movement stroke in the previous study. The coordinate information of the separated consonants and vowels are converted into image data, and Korean handwriting recognition was performed using a convolutional neural network. After learning the neural network using 1,680 syllables written by five hand writers, the accuracy is calculated by using the new hand writers who did not participate in the writing of training data. The accuracy of phoneme-based recognition is 98.9% based on convolutional neural network. The proposed method has the advantage of drastically reducing learning data compared to syllable-based learning.

• 한국전자통신학회논문지
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• 제19권2호
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• pp.397-402
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• 2024

음성 인식과 모션 인식 기술은 다양한 스마트 디바이스에 적용되어 사용되고 있으나, 단순한 명령어 인식 형태로 구성되어 단순 기능으로 사용되고 있다. 인식 데이터에 대한 단순 기능에서 벗어나 다양한 분야에서 학습된 데이터를 기반으로 전문적인 명령어 수행 능력이 요구되고 있다. 현재 세계적으로 경쟁이 이루어지고 있는 생성형 AI를 활용하여 사용자에게 최적의 데이터를 제공하고, 음성 인식과 모션 인식을 통해 상호작용할 수 있는 시스템 플랫폼에 대한 연구가 진행되고 있다. 본 연구를 위해 설계한 주요 기술 프로세스는 음성 및 모션 인식 기능, AI 기술 적용, 양방향 커뮤니케이션 등 기술을 이용한 설계하였다. 본 논문에서는 AI 기술을 적용한 디바이스와 음성인식과 모션 인식 기술을 통해 디바이스와 사용자 간 양방향 커뮤니케이션을 다양한 입력방식에 의해 이루어질 수 있도록 하였다.

• 전자통신동향분석
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• 제36권4호
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• pp.92-102
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• 2021

Outdoor sports activities have been restricted by serious air pollution, such as fine dust and yellow dust, and abnormal meteorological change, such as heatwave and heavy snow. These environmental problems have rapidly increased the demand for indoor sports activities. Virtual sports, such as virtual golf, virtual baseball, virtual soccer, etc., allow playing various sports games without going outdoors. Indoor sports industries and markets have seen rapid growth since the advent of virtual sports. Most virtual sports platforms use screen-based virtual reality techniques, which are why they are called screen sports. However, these platforms cannot support various sports games, especially virtual match games, such as squash, boxing, and so on, because existing screen-based virtual reality sports techniques use real balls and players. This article presents screen-based haptic-augmented reality technologies for a new virtual sports platform. The new platform does not use real balls and players to solve the limitations of previous platforms. Here, various technologies, including human motion tracking, human action recognition, haptic feedback, screen-based augmented-reality systems, and augmented-reality sports content, are unified for the new virtual sports platform. From these haptic-augmented reality technologies, the proposed platform supports sports games, including indoor virtual matches, that existing virtual sports platforms cannot support.

• 전기학회논문지
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• 제58권11호
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• pp.2275-2280
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• 2009

u-Healthcare service is one of attractive applications in ubiquitous environment. In this paper, we propose a method to recognize exercises using a new accelerometer based body-attached platform for supporting u-Healthcare service. The platform consists of a device for measuring accelerometer data and a device for receiving the data. The former measures a user's motion data using a 3-axis accelerometer. The latter transmits the accelerometer data to a computer for recognizing the user's exercise. The algorithm for exercise recognition classifies the type of exercise using principle components analysis(PCA) from the accelerometer data transformed by discrete fourier transform(DFT), and estimates the repetition count of the recognized exercise using a peak detection algorithm. We evaluate the performance of the algorithm from the accuracy of the recognition of exercise type and the error rate of the estimation of repetition count. In our experimental result, the algorithm shows the accuracy about 98%.

• 전자통신동향분석
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• 제33권3호
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• pp.89-99
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• 2018

Human activity recognition (HAR) is a technology that aims to offer an automatic recognition of what a person is doing with respect to their body motion and gestures. HAR is essential in many applications such as human-computer interaction, health care, rehabilitation engineering, video surveillance, and artificial intelligence. Smartphones are becoming the most popular platform for activity recognition owing to their convenience, portability, and ease of use. The noticeable change in smartphone-based activity recognition is the adoption of a deep learning algorithm leading to successful learning outcomes. In this article, we analyze the technology trend of activity recognition using smartphone sensors, challenging issues for future development, and a strategy change in terms of the generation of a activity recognition dataset.

• 한국멀티미디어학회논문지
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• 제24권3호
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• pp.355-372
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• 2021

Recently, high-performance HMDs (Head-Mounted Display) are becoming wireless due to the growth of virtual reality technology. Accordingly, environmental constraints on the hardware usage are reduced, enabling multiple users to experience virtual reality within a single space simultaneously. Existing multi-user virtual reality platforms use the user's location tracking and motion sensing technology based on vision sensors and active markers. However, there is a decrease in immersion due to the problem of overlapping markers or frequent matching errors due to the reflected light. Goal of this study is to develop a multi-user virtual reality moving platform in a single space that can resolve sensing errors and user immersion decrease. In order to achieve this goal hybrid sensing technology was developed, which is the convergence of vision sensor technology for position tracking, IMU (Inertial Measurement Unit) sensor motion capture technology and gesture recognition technology based on smart gloves. In addition, integrated safety operation system was developed which does not decrease the immersion but ensures the safety of the users and supports multimodal feedback. A 6 m×6 m×2.4 m test bed was configured to verify the effectiveness of the multi-user virtual reality moving platform for four users.

• 한국CDE학회논문집
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• 제18권4호
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• pp.290-298
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• 2013

A walking navigation system (usually known as a locomotion interface) is an interactive platform which gives simulated walking sensation to users using sensed bipedal motion signals. This enables us to perform navigation tasks using only bipedal movement. Especially, it is useful for the certain VR task which emphasizes on physical human movement, or accompanies understanding of the size and complexity of building structures. In this work, we described system components of VR walking system and investigated several types of walking platform by literature survey. We adopted a MS Kinect depth sensor for the motion recognition and a treadmill which includes directional turning mechanism for the walking platform. Through the integration of these components with a VR navigation scenario, we developed a simple VR walking navigation system. Finally several technical issues were found during development process, and further research directions were suggested for the system improvement.

• 융합신호처리학회논문지
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• 제18권1호
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• pp.6-12
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• 2017

본 논문에서는 모션인식을 활용한 Human UI/UX를 위한 IoT 기반 스마트헬스 서비스를 제안한다. 현재까지 M2M기반의 u-healthcare에서 적용되는 센서 네트워크에서는 TCP/IP 프로토콜이 아닌 non-IP프로토콜을 이용하고 있다. 그러나 서비스의 이용 확대와 IoT 기반의 센서 네트워크 관리를 용이하게 하기 위해서는 다수의 센서들의 인터넷 연결이 반드시 요구된다. 따라서 센서들에 의하여 측정된 자료들을 인터넷과 통신하는 것은 물론 이동이 가능해야 하기 때문에 네트워크 이동성을 고려한 IoT 기반 스마트헬스 서비스를 설계하였다. 또한 IoT 기반 스마트헬스 서비스는 기존의 헬스케어 플랫폼과는 다르게 바이오 정보뿐만이 아니라 동작감지를 위한 스마트 헬스 서비스를 개발하였다. u-healthcare에서 사용되는 WBAN 통신은 일반적으로 많은 네트워크화된 장치 및 게이트웨이로 구성된다. 본 논문에서 제안하는 방법은 WBAN 센서 노드간의 이동성을 지원하는 기술을 이용함으로써 무선 환경에서도 동적 변화에 쉽게 대응할 수 있고, 사용자의 동작감지를 통해 체계적인 관리가 이루어진다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.757-770
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• 2020

We propose a hand gesture recognition method that is compatible with a head-up display (HUD) including small processing resource. For fast link adaptation with HUD, it is necessary to rapidly process gesture recognition and send the minimum amount of driver hand gesture data from the wearable device. Therefore, we use a method that recognizes each hand gesture with an inertial measurement unit (IMU) sensor based on revised correlation matching. The method of gesture recognition is executed by calculating the correlation between every axis of the acquired data set. By classifying pre-defined gesture values and actions, the proposed method enables rapid recognition. Furthermore, we evaluate the performance of the algorithm, which can be implanted within wearable bands, requiring a minimal process load. The experimental results evaluated the feasibility and effectiveness of our decomposed correlation matching method. Furthermore, we tested the proposed algorithm to confirm the effectiveness of the system using pre-defined gestures of specific motions with a wearable platform device. The experimental results validated the feasibility and effectiveness of the proposed hand gesture recognition system. Despite being based on a very simple concept, the proposed algorithm showed good performance in recognition accuracy.