• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.143-150
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• 2017

In this paper, we propose a novel motion recognition platform using smart-phone tracking and color communication. The interface requires only a camera and a personal smart-phone to provide a motion control interface rather than expensive equipment. The platform recognizes the user's gestures by the tracking 3D distance and the rotation angle of the smart-phone, which acts essentially as a motion controller in the user's hand. Also, a color coded communication method using RGB color combinations is included within the interface. Users can conveniently send or receive any text data through this function, and the data can be transferred continuously even while the user is performing gestures. We present the result that implementation of viable contents based on the proposed motion recognition platform.

• Journal of Platform Technology
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• v.6 no.1
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.397-402
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• 2024

Speech recognition and motion recognition technologies are applied and used in various smart devices, but they are composed of simple command recognition forms and are used as simple functions. Apart from simple functions for recognition data, professional command execution capabilities are required based on data learned in various fields. Research is being conducted on a system platform that provides optimal data to users using Generative AI, which is currently competing around the world, and can interact through voice recognition and motion recognition. The main technical processes designed for this study were designed using technologies such as voice and motion recognition functions, application of AI technology, and two-way communication. In this paper, two-way communication between a device and a user can be achieved by various input methods through voice recognition and motion recognition technology applied with AI technology.

• Electronics and Telecommunications Trends
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• v.36 no.4
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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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%.

• Electronics and Telecommunications Trends
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• v.33 no.3
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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.

• Journal of Korea Multimedia Society
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• v.24 no.3
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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.

• Korean Journal of Computational Design and Engineering
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• v.18 no.4
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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.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.1
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• pp.6-12
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• 2017

In this paper, we proposed IoT based Smart Health Service using Motion Recognition for Human UX/UI. Until now, sensor networks using M2M-based u-healthcare are using non-IP protocol instead of TCP / IP protocol. However, in order to increase the service utilization and facilitate the management of the IoT-based sensor network, many sensors are required to be connected to the Internet. Therefore, IoT-based smart health service is designed considering network mobility because it is necessary to communicate not only the data measured by sensors but also the Internet. In addition, IoT-based smart health service developed smart health service for motion detection as well as bio information unlike existing healthcare platform. WBAN communications used in u-healthcare typically consist of many networked devices and gateways. The method proposed in this paper can easily cope with dynamic changes even in a wireless environment by using a technology supporting mobility between WBAN sensor nodes, and systematic management is performed through detection of a user's motion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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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.