• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.674-679
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• 2006

In this paper a gesture recognition system using 3D feature data is described. The system relies on a novel 3D sensor that generates a dense range mage of the scene. The main novelty of the proposed system, with respect to other 3D gesture recognition techniques, is the capability for robust recognition of complex hand postures such as those encountered in sign language alphabets. This is achieved by explicitly employing 3D hand features. Moreover, the proposed approach does not rely on colour information, and guarantees robust segmentation of the hand under various illumination conditions, and content of the scene. Several novel 3D image analysis algorithms are presented covering the complete processing chain: 3D image acquisition, arm segmentation, hand -forearm segmentation, hand pose estimation, 3D feature extraction, and gesture classification. The proposed system is tested in an application scenario involving the recognition of sign-language postures.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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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%.

• Research in Community and Public Health Nursing
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• v.12 no.1
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• pp.278-287
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• 2001

The purpose of this study is to provide with basic information on application of hand acupuncture as a complementary and alternative therapy by giving some recognition of efficiency and effectiveness of hand acupuncture. And so, answers for questionnaires of 290 respondents were used for this research and collected from June 5 through 13, 1999 from adults twenty and over who were participating in the hand acupuncture training program in Seoul and had some direct experiences with hand acupuncture therapy, whatever they had been treated and/or had treated. To secure reliability of measurement tool. Cronbach'a has been calculated and Factor Analysis was done as Validity Analysis of question classification. Demograprucal characteristics of hand acupuncture experienced people and factors related to hand acupuncture experiences are calculated based on the real number and percentage. The degree of recognition of efficiency and effectiveness of hand acupuncture is made as average and standard deviation, while the degree of recognition of efficiency and effectiveness based on general characteristics come from one-way ANOVA. 1. According to socio-demographical analysis. the questioned could be classified firstly as age (40-49 : 32.5%. 30-39 : 24.9%. 50-59 : 21.9%. 60-69 : 14.7%. 20-29 : 6.0%). secondly gender (male 36.6%. female 63.4%). thirdly occupation (housewife: 43.8%. self-employed: 15.5%. company-employee: 14.8%). fourthly education (high school graduate: 41.9%, college graduate: 37.9%), and lastly monthly-income (1 to 2 million: 51.4%. 2 to 3 million: 20,3%) 2, As for the general aspects related to hand acupuncture. 80,0% of the respondents answered almost zero for the monthly average number of visit to hospital and 15.5% responded 1 to 2 visits, 6,2% of the respondents is complaining of a disorder of digestive system. 19,0% circulatory disease, 10.7% bad nervous system. By utilizing hand acupuncture, 84% of the questioned have following experiences in curing diseases: digestive system 47.3%, circulatory system 9.3%, nervous system 8.3%, 54,1% are curing 1 to 2 and 10.3% 3 to 4 patients on a daily basis with hand acupuncture. Research on the demerits of giving medical treatment with hand acupuncture shows 23,8% are feeling economic burden. 16.6% difficulty of learning and 16.2% weak theoretical backgrounds. 3. Among the efficiency recognition, possibility of general application is average 4,29 and simple treatment is 4,19. economic merits 4.36. possibility of establishment with supplementary and alternative medicine 4.17, medical effectiveness 4.09. 4, As a result of demographical analysis on the efficiency and effectiveness of hand acupuncture therapy, it appears that the recognition of efficiency based on occupation and the recognition of effectiveness based on monthly income are most significant to be noticed. In an orderly fashion. government-employee, self-employed, company-employee. and then housewife have perceived hand acupuncture very efficiently, And those who recognize hand acupuncture to be most effective are people earn 1 million to 2 million won a month, 5. The efficiency(p = .003) and effectiveness (p= .049) of hand acupuncture therapy by number of visit to hospital were statiscally significant, and effectiveness of hand acupuncture therapy by disease exist was statiscally significant (p= .033).

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.393-400
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• 2013

Hand shape recognition is a fundamental technique for implementing natural human-computer interaction. In this paper, we discuss a method for effectively detecting a hand region in Kinect-based hand shape recognition. Since Kinect is a camera that can capture color images and infrared images (or depth images) together, both images can be exploited for the process of detecting a hand region. That is, a hand region can be detected by finding pixels having skin colors or by finding pixels having a specific depth. Therefore, after analyzing the performance of each, we need a method of properly combining both to clearly extract the silhouette of hand region. This is because the hand shape recognition rate depends on the fineness of detected silhouette. Finally, through comparison of hand shape recognition rates resulted from different hand region detection methods in general environments, we propose a high-performance hand region detection method.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.263-266
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• 2007

We introduce a vision-based hand gesture recognition fer understanding musical time and patterns without extra special devices. We suggest a simple and reliable vision-based hand gesture recognition having two features First, the motion-direction code is proposed, which is a quantized code for motion directions. Second, the conducting feature point (CFP) where the point of sudden motion changes is also proposed. The proposed hand gesture recognition system extracts the human hand region by segmenting the depth information generated by stereo matching of image sequences. And then, it follows the motion of the center of the gravity(COG) of the extracted hand region and generates the gesture features such as CFP and the direction-code finally, we obtain the current timing pattern of beat and tempo of the playing music. The experimental results on the test data set show that the musical time pattern and tempo recognition rate is over 86.42% for the motion histogram matching, and 79.75% fer the CFP tracking only.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.424-431
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• 2019

In this paper, we propose a real-time hand gesture recognition algorithm to eliminate the inconvenience of using hand controllers in VR applications. The user's 3D hand coordinate information is detected by leap motion sensor and then the coordinates are generated into two dimensional image. We classify hand gestures in real-time by learning the imaged 3D hand coordinate information through SSD(Single Shot multibox Detector) model which is one of CNN(Convolutional Neural Networks) models. We propose to use all 3 channels rather than only one channel. A sliding window technique is also proposed to recognize the gesture in real time when the user actually makes a gesture. An experiment was conducted to measure the recognition rate and learning performance of the proposed model. Our proposed model showed 99.88% recognition accuracy and showed higher usability than the existing algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.9
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• pp.935-944
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• 2006

EMG pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study is to develop an efficient feature projection method for EMC pattern recognition. To this end, we propose a linear supervised feature projection that utilizes linear discriminant analysis (LDA). We first perform wavelet packet transform (WPT) to extract the feature vector from four channel EMC signals. For dimensionality reduction and clustering of the WPT features, the LDA incorporates class information into the learning procedure, and finds a linear matrix to maximize the class separability for the projected features. Finally, the multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of LDA for the WPT features, we compare LDA with three other feature projection methods. From a visualization and quantitative comparison, we show that LDA has better performance for the class separability, and the LDA-projected features improve the classification accuracy with a short processing time. We implemented a real-time pattern recognition system for a multifunction myoelectric hand. In experiment, we show that the proposed method achieves 97.2% recognition accuracy, and that all processes, including the generation of control commands for myoelectric hand, are completed within 97 msec. These results confirm that our method is applicable to real-time EMG pattern recognition far myoelectric hand control.

• Culinary science and hospitality research
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• v.12 no.4 s.31
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• pp.269-283
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• 2006

To show the recognition of hand-sanitizer, we studied the answers of research questions where we got from the northern part of Seoul and Kyunggi Province. We had categorized two groups both industry-related people who work in restaurants, hygiene service shops, whole sales, government organizations, PC shops, factory-department stores and non industry-related people who work in schools, general offices for this study. 1. Hand sanitation level: Over 60% people washed hands 6 times a day. The group using water and soap was much bigger than the group using water for washing hands. For drying, people preferred wipe tissue, towels, clothes, non drying in that order. 2. Recognition of hand sanitizer and its usage experience: Most people(66.5%) did not know what hand sanitizer is, but they have positive attitude if they use this machine. 3. Place of hand sanitizer: The proper places to install were such public places as hospitals, restrooms, and restaurants. The fifties-group was the most frequently hand washing generation with over 9 times a day. 4. Comparison of recognition for hand-sanitizer by male and female: There were different results in each evaluation item by either male or female. The frequency and method of hand washing showed high in males, while females observed hand sanitization, installation requirements, installation areas, home installation, etc more than anything else.

• 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.

• 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.