• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.593-599
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• 2012

Objective: This study aims to implement a non-contact gesture-based interface for presentation purposes and to analyze the effect of the proposed interface as information transfer assisted device. Background: Recently, research on control device using gesture recognition or speech recognition is being conducted with rapid technological growth in UI/UX area and appearance of smart service products which requires a new human-machine interface. However, few quantitative researches on practical effects of the new interface type have been done relatively, while activities on system implementation are very popular. Method: The system presented in this study is implemented with KINECT$^{(R)}$ sensor offered by Microsoft Corporation. To investigate whether the proposed system is effective as a presentation support tool or not, we conduct experiments by giving several lectures to 40 participants in both a traditional lecture room(keyboard-based presentation control) and a non-contact gesture-based lecture room(KINECT-based presentation control), evaluating their interests and immersion based on contents of the lecture and lecturing methods, and analyzing their understanding about contents of the lecture. Result: We check that whether the gesture-based presentation system can play effective role as presentation supporting tools or not depending on the level of difficulty of contents using ANOVA. Conclusion: We check that a non-contact gesture-based interface is a meaningful tool as a sportive device when delivering easy and simple information. However, the effect can vary with the contents and the level of difficulty of information provided. Application: The results presented in this paper might help to design a new human-machine(computer) interface for communication support tools.

• Journal of IKEEE
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• v.23 no.4
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• pp.1423-1429
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• 2019

Recently, a system that supports efficient interaction, a human machine interface (HMI), has become a hot topic. In this paper, we propose a new real time low-cost hand gesture control system as one of vehicle interaction methods. In order to reduce computation time, depth information was acquired using a time-of-flight (TOF) camera because it requires a large amount of computation when detecting hand regions using an RGB camera. In addition, fourier descriptor were used to reduce the learning model. Since the Fourier descriptor uses only a small number of points in the whole image, it is possible to miniaturize the learning model. In order to evaluate the performance of the proposed technique, we compared the speeds of desktop and raspberry pi2. Experimental results show that performance difference between small embedded and desktop is not significant. In the gesture recognition experiment, the recognition rate of 95.16% is confirmed.

• Journal of the Ergonomics Society of Korea
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• v.36 no.4
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• pp.267-280
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• 2017

Objective: The aim of this study is to investigate key user experience factors of interactions for Head Mounted Display (HMD) devices in the Virtual Reality Environment (VRE). Background: Virtual reality interaction research has been conducted steadily, while interaction methods and virtual reality devices have improved. Recently, all of the virtual reality devices are head mounted display based ones. Also, HMD-based interaction types include Remote Controller, Head Tracking, and Hand Gesture. However, there is few study on usability evaluation of virtual reality. Especially, the usability of HMD-based virtual reality was not investigated. Therefore, it is necessary to study the usability of HMD-based virtual reality. Method: HMD-based VR devices released recently have only three interaction types, 'Remote Controller', 'Head Tracking', and 'Hand Gesture'. We search 113 types of research to check the user experience factors or evaluation scales by interaction type. Finally, the key user experience factors or relevant evaluation scales are summarized considering the frequency used in the studies. Results: There are various key user experience factors by each interaction type. First, Remote controller's key user experience factors are 'Ease of learning', 'Ease of use', 'Satisfaction', 'Effectiveness', and 'Efficiency'. Also, Head tracking's key user experience factors are 'Sickness', 'Immersion', 'Intuitiveness', 'Stress', 'Fatigue', and 'Ease of learning'. Finally, Hand gesture's key user experience factors are 'Ease of learning', 'Ease of use', 'Feedback', 'Consistent', 'Simple', 'Natural', 'Efficiency', 'Responsiveness', 'Usefulness', 'Intuitiveness', and 'Adaptability'. Conclusion: We identified key user experience factors for each interaction type through literature review. However, we did not consider objective measures because each study adopted different performance factors. Application: The results of this study can be used when evaluating HMD-based interactions in virtual reality in terms of usability.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1393-1402
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• 2013

Hand gesture is one of the most popular communication methods in everyday life. In human-computer interaction applications, hand gesture recognition provides a natural way of communication between humans and computers. There are mainly two methods of hand gesture recognition: glove-based method and vision-based method. In this paper, we propose a vision-based hand gesture recognition method using Kinect. By using the depth information is efficient and robust to achieve the hand detection process. The finger labeling makes the system achieve pose classification according to the finger name and the relationship between each fingers. It also make the classification more effective and accutate. Two kinds of gesture sets can be recognized by our system. According to the experiment, the average accuracy of American Sign Language(ASL) number gesture set is 94.33%, and that of general gestures set is 95.01%. Since our system runs in real-time and has a high recognition rate, we can embed it into various applications.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.186-191
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• 2015

Hand gesture is the most common tool used to interact with and control various electronic devices. In this paper, we propose a novel hand gesture recognition method using fuzzy logic based classification with a new type of sensor array. In some cases, feature patterns of hand gesture signals cannot be uniquely distinguished and recognized when people perform the same gesture in different ways. Moreover, differences in the hand shape and skeletal articulation of the arm influence to the process. Manifold features were extracted, and efficient features, which make gestures distinguishable, were selected. However, there exist similar feature patterns across different hand gestures, and fuzzy logic is applied to classify them. Fuzzy rules are defined based on the many feature patterns of the input signal. An adaptive neural fuzzy inference system was used to generate fuzzy rules automatically for classifying hand gestures using low number of feature patterns as input. In addition, emotion expression was conducted after the hand gesture recognition for resultant human-robot interaction. Our proposed method was tested with many hand gesture datasets and validated with different evaluation metrics. Experimental results show that our method detects more hand gestures as compared to the other existing methods with robust hand gesture recognition and corresponding emotion expressions, in real time.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.171.4-171
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• 2001

This paper proposes a method of human- robot interaction (HRI) using face directional gesture. A single CCD color camera is used to input face region, and the robot recognizes the face directional gesture based on the facial feature´s positions. One can give a command such as stop, go, left and right turn to the robot using the face directional gesture. Since the robot also has the ultra sonic sensors, it can detect obstacles and determine a safe direction at the current position. By combining the user´s command with the sensed obstacle configuration, the robot selects the safe and efficient motion direction. From simulation results, we show that the robot with HRI is more reliable for the robot´s navigation.

• ETRI Journal
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• v.34 no.3
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• pp.466-469
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• 2012

In this letter, we propose a dual autostereoscopic display platform employing a natural interaction method, which will be useful for sharing visual data with users. To provide 3D visualization of a model to users who collaborate with each other, a beamsplitter is used with a pair of autostereoscopic displays, providing a visual illusion of a floating 3D image. To interact with the virtual object, we track the user's hands with a depth camera. The gesture recognition technique we use operates without any initialization process, such as specific poses or gestures, and supports several commands to control virtual objects by gesture recognition. Experiment results show that our system performs well in visualizing 3D models in real-time and handling them under unconstrained conditions, such as complicated backgrounds or a user wearing short sleeves.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.1-7
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• 2013

Body gesture Recognition has been one of the interested research field for Human-Robot Interaction(HRI). Most of the conventional body gesture recognition algorithms used Hidden Markov Model(HMM) for modeling gestures which have spatio-temporal variabilities. However, HMM-based algorithms have difficulties excluding meaningless gestures. Besides, it is necessary for conventional body gesture recognition algorithms to perform gesture segmentation first, then sends the extracted gesture to the HMM for gesture recognition. This separated system causes time delay between two continuing gestures to be recognized, and it makes the system inappropriate for continuous gesture recognition. To overcome these two limitations, this paper suggests primitive body model encoding, which performs spatio/temporal quantization of motions from human body model and encodes them into predefined primitive codes for each link of a body model, and Selective/Asynchronous Input-Parallel State machine(SAI-PSM) for multiple-simultaneous gesture recognition. The experimental results showed that the proposed gesture recognition system using primitive body model encoding and SAI-PSM can exclude meaningless gestures well from the continuous body model data, while performing multiple-simultaneous gesture recognition without losing recognition rates compared to the previous HMM-based work.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3924-3940
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• 2020

Dynamic hand gesture recognition(d-HGR) plays an important role in human-computer interaction(HCI) system. With the growth of hand-pose estimation as well as 3D depth sensors, depth, and the hand-skeleton dataset is proposed to bring much research in depth and 3D hand skeleton approaches. However, it is still a challenging problem due to the low resolution, higher complexity, and self-occlusion. In this paper, we propose a hand-shape feature extraction(HSFE) network to produce robust hand-shapes. We build a hand-shape model, and hand-skeleton based on LSTM to exploit the temporal information from hand-shape and motion changes. Fusion between two models brings the best accuracy in dynamic hand gesture (DHG) dataset.

• Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.26-37
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• 2015

A lot of researchers have been investigating interactive portable projection systems such as a mini-projector. In addition, in exhibition halls and museums, there is a trend toward using interactive projection systems to make viewing more exciting and impressive. They can also be applied in the field of art, for example, in creating shadow plays. The key idea of the interactive portable projection systems is to recognize the user's gesture in real-time. In this paper, a vision-based shadow gesture recognition method is proposed for interactive projection systems. The gesture recognition method is based on the screen image obtained by a single web camera. The method separates only the shadow area by combining the binary image with an input image using a learning algorithm that isolates the background from the input image. The region of interest is recognized with labeling the shadow of separated regions, and then hand shadows are isolated using the defect, convex hull, and moment of each region. To distinguish hand gestures, Hu's invariant moment method is used. An optical flow algorithm is used for tracking the fingertip. Using this method, a few interactive applications are developed, which are presented in this paper.