• 한국멀티미디어학회논문지
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• 제17권11호
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• pp.1263-1269
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• 2014

As rapid development of new hardware control interface technology, new concepts have been being proposed and emerged. In this paper, a new approach based on leap motion system is proposed. While we employ a position information from sensor, the hand gesture recognition is suggested with the pre-defined patterns. To do this, we design a recognition algorithm with hand gesture and finger patterns. We apply the proposed scheme to 3-dimensional avatar controling and editing software tool for making animation in the cyber space as a representative application. This proposed algorithm can be used to control computer systems in medical treatment, game, education and other various areas.

• 대한인간공학회지
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• 제31권4호
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• pp.577-583
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• 2012

Objective: This paper describes the process and results related to the development of gesture recognition-based natural user interface(NUI) for vehicle maintenance e-Training system. Background: E-Training refers to education training that acquires and improves the necessary capabilities to perform tasks by using information and communication technology(simulation, 3D virtual reality, and augmented reality), device(PC, tablet, smartphone, and HMD), and environment(wired/wireless internet and cloud computing). Method: Palm movement from depth camera is used as a pointing device, where finger movement is extracted by using OpenCV library as a selection protocol. Results: The proposed NUI allows trainees to control objects, such as cars and engines, on a large screen through gesture recognition. In addition, it includes the learning environment to understand the procedure of either assemble or disassemble certain parts. Conclusion: Future works are related to the implementation of gesture recognition technology for a multiple number of trainees. Application: The results of this interface can be applied not only in e-Training system, but also in other systems, such as digital signage, tangible game, controlling 3D contents, etc.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4491-4509
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• 2017

The present paper proposes a novel dynamic system for hand gesture recognition. The approach involved is comprised of three main steps: detection, tracking and recognition. First, the gesture contour captured by a 2D-camera is detected by combining the three-frame difference method and skin-color elliptic boundary model. Then, the trajectory of the hand gesture is extracted via a gesture-tracking algorithm based on an occlusion-direction oriented linear extrapolation predictor, where the gesture coordinate in next frame is predicted by the judgment of current occlusion direction. Finally, to overcome the interference of insignificant trajectory segments, the longest common subsequence (LCS) is employed with the aid of velocity information. Besides, to tackle the subgesture problem, i.e., some gestures may also be a part of others, the most probable gesture category is identified through comparison of the relative LCS length of each gesture, i.e., the proportion between the LCS length and the total length of each template, rather than the length of LCS for each gesture. The gesture dataset for system performance test contains digits ranged from 0 to 9, and experimental results demonstrate the robustness and effectiveness of the proposed approach.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.832-837
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• 2007

Electroencephalographic(EEG) is used to record activities of human brain in the area of psychology for many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• 제어로봇시스템학회논문지
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• 제20권6호
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• pp.593-598
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• 2014

Hand gesture recognition based on vision is a challenging task in human-robot interaction. The sign language of finger spelling alphabets has been tested as a kind of hand gesture. In this paper, we test hand gesture recognition by detecting the contour shape and orientation of hand with visual image. The method has three stages, the first stage of finding hand component separated from the background image, the second stage of extracting the contour feature over the hand component and the last stage of comparing the feature with the reference features in the database. Here, finger spelling alphabets are used to verify the performance of our system and our method shows good performance to discriminate finger alphabets.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.186-192
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• 2021

In this paper, we evaluated prediction accuracy of Euler angle spectrograph classification method using a convolutional neural networks (CNN) for hand gesture recognition in augmented reality (AR) cognitive rehabilitation system based on Leap Motion Controller (LMC). Hand gesture recognition methods using a conventional support vector machine (SVM) show 91.3% accuracy in multiple motions. In this paper, five hand gestures ("Promise", "Bunny", "Close", "Victory", and "Thumb") are selected and measured 100 times for testing the utility of spectral classification techniques. Validation results for the five hand gestures were able to be correctly predicted 100% of the time, indicating superior recognition accuracy than those of conventional SVM methods. The hand motion recognition using CNN meant to be applied more useful to AR cognitive rehabilitation training systems based on LMC than sign language recognition using SVM.

• 제어로봇시스템학회논문지
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• 제21권12호
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• pp.1100-1108
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• 2015

A camera-based real-time hand posture and gesture recognition system is proposed for controlling various devices inside automobiles. It uses an imaging system composed of a camera with a proper filter and an infrared lighting device to acquire images of hand-motion sequences. Several steps of pre-processing algorithms are applied, followed by a background normalization process before segmenting the hand from the background. The hand posture is determined by first separating the fingers from the main body of the hand and then by finding the relative position of the fingers from the center of the hand. The beginning and ending of the hand motion from the sequence of the acquired images are detected using pre-defined motion rules to start the hand gesture recognition. A set of carefully designed features is computed and extracted from the raw sequence and is fed into a decision tree-like decision rule for determining the hand gesture. Many experiments are performed to verify the system. In this paper, we show the performance results from tests on the 550 sequences of hand motion images collected from five different individuals to cover the variations among many users of the system in a real-time environment. Among them, 539 sequences are correctly recognized, showing a recognition rate of 98%.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.126.3-126
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• 2001

A new method to govern the navigation of a mobile robot is proposed based on the following two procedures: one is to achieve vision information by using a 2 D-O-F camera as a communicating medium between a man and a mobile robot and the other is to analyze and to behave according to the recognized hand gesture commands. In the previous researches, mobile robots are passively to move through landmarks, beacons, etc. To incorporate various changes of situation, a new control system manages the dynamical navigation of a mobile robot. Moreover, without any generally used expensive equipments or complex algorithms for hand gesture recognition, a reliable hand gesture recognition system is efficiently implemented to convey the human commands to the mobile robot with a few constraints.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.7-10
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• 2008

자동차의 수가 점점 증가함에 따라 교통사고도 그 만큼 증가하고 있다. 교통사고의 주요 원인 중 하나가 졸음운전이나 부주의한 운전에 의한 것이다. 따라서 Real-Time으로 운전자의 제스처를 인식하여 졸음운전이나 부주의에 의한 사고를 사전에 예방하여 보다 안전한 운전을 돕는 서비스가 필요시 되고 있다. 본 논문에서는 운전자의 제스처 인식에 전처리 과정으로 운전자의 상반신에 대한 영상데이터에서 Adaboost를 이용하여 복잡한 배경과 다양한 환경에서 강인하게 얼굴 영역을 찾는 알고리즘을 소개한다.

• 제어로봇시스템학회논문지
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• 제21권2호
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• pp.150-156
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• 2015

In this paper, a marionette control system using wrist and finger gestures through an IMU sensor is studied. The signals from the sensor device are conditioned and recognized, then the commands are sent to the 8 motors of the marionette via Bluetooth (5 motors control the motion of the marionette, and 3 motors control the location of the marionette). It is revealed that the degree of freedom of fingers are not independent from each other, therefore, some gestures are hardly made. Gesture mode changes for difficult postures of the fingers in cases of a lack of finger DOF are proposed. Therefore, the gesture mode change switches the assignment of gesture as required. Experimental results show that gesture mode change is successful for appropriate postures of a marionette.