• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.664-667
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• 2009

There is strong demand to create wearable PC systems that can support the user outdoors. When we are outdoors, our movement makes it impossible to use traditional input devices such as keyboards and mice. We propose a hand gesture interface based on image processing to operate wearable PCs. The semi-transparent PC screen is displayed on the head mount display (HMD), and the user makes hand gestures to select icons on the screen. The user's hand is extracted from the images captured by a color camera mounted above the HMD. Since skin color can vary widely due to outdoor lighting effects, a key problem is accurately discrimination the hand from the background. The proposed method does not assume any fixed skin color space. First, the image is divided into blocks and blocks with similar average color are linked. Contiguous regions are then subjected to hand recognition. Blocks on the edges of the hand region are subdivided for more accurate finger discrimination. A change in hand shape is recognized as hand movement. Our current input interface associates a hand grasp with a mouse click. Tests on a prototype system confirm that the proposed method recognizes hand gestures accurately at high speed. We intend to develop a wider range of recognizable gestures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.618-632
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• 2010

This paper presents a novel approach to control the augmented reality (AR) objects robustly in a marker-less AR system by fingertip tracking and hand pattern recognition. It is known that one of the promising ways to develop a marker-less AR system is using human's body such as hand or face for replacing traditional fiducial markers. This paper introduces a real-time method to manipulate the overlaid virtual objects dynamically in a marker-less AR system using both hands with a single camera. The left bare hand is considered as a virtual marker in the marker-less AR system and the right hand is used as a hand mouse. To build the marker-less system, we utilize a skin-color model for hand shape detection and curvature-based fingertip detection from an input video image. Using the detected fingertips the camera pose are estimated to overlay virtual objects on the hand coordinate system. In order to manipulate the virtual objects rendered on the marker-less AR system dynamically, a vision-based hand control interface, which exploits the fingertip tracking for the movement of the objects and pattern matching for the hand command initiation, is developed. From the experiments, we can prove that the proposed and developed system can control the objects dynamically in a convenient fashion.

• Fashion & Textile Research Journal
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• v.10 no.6
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• pp.966-978
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• 2008

The aim of this study was to provide size intervals in hand requisite in design of gloves. In this study, a survey was administered to gather information about glove manufacturers' sizing systems. In addition, KS glove standards' size dimensions were analyzed. As well, the ISO hand sizing system was also studied. Based on all the analyses' results, a new glove size intervals were composed. The size comprised the control dimensions of hand length and hand circumference. The size interval was 8mm in hand length and 13mm in hand circumference. The size range was established by making the coverage above 80%. The coverage of the new size interval system for an adult's hand was 86.4% and 13 sizes were suggested. The coverage of the male size system was 86.0% and 10 sizes were suggested. The coverage of the female size system was 87.6% and 8 sizes were suggested. For the unfitted gloves, size ranges based on hand length and hand circumference were developed. For the adults group, S, M, L, and XL were suggested and the coverage of the new size range was 78.8%. For the male group, S, M, and L were suggested and the coverage was 82.3%. For the female group, S, M, and L were also suggested and the coverage was 81.3%.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.368-374
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• 2011

This paper proposes a robot control human interface using Markov model (HMM) based hand signal recognizer. The command receiving humanoid robot sends webcam images to a client computer. The client computer then extracts the intended commanding hum n's hand motion descriptors. Upon the feature acquisition, the hand signal recognizer carries out the recognition procedure. The recognition result is then sent back to the robot for responsive actions. The system performance is evaluated by measuring the recognition of '48 hand signal set' which is created randomly using fundamental hand motion set. For isolated motion recognition, '48 hand signal set' shows 97.07% recognition rate while the 'baseline hand signal set' shows 92.4%. This result validates the proposed hand signal recognizer is indeed highly discernable. For the '48 hand signal set' connected motions, it shows 97.37% recognition rate. The relevant experiments demonstrate that the proposed system is promising for real world human-robot interface application.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.504-516
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• 2011

In this paper, we will present a method to detect human hand and recognize hand gesture. For detecting the hand region, we use the feature of human skin color and hand feature (with boundary complexity) to detect the hand region from the input image; and use algorithm of optical flow to track the hand movement. Hand gesture recognition is composed of two parts: 1. Posture recognition and 2. Motion recognition, for describing the hand posture feature, we employ the Fourier descriptor method because it's rotation invariant. And we employ PCA method to extract the feature among gesture frames sequences. The HMM method will finally be used to recognize these feature to make a final decision of a hand gesture. Through the experiment, we can see that our proposed method can achieve 99% recognition rate at environment with simple background and no face region together, and reduce to 89.5% at the environment with complex background and with face region. These results can illustrate that the proposed algorithm can be applied as a production.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.1
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• pp.1-7
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• 2013

This study is aimed to investigate the association between anaerobic-aerobic exercise intensity and hand steadiness. Hand steadiness is the decisive contributor to affecting the job performance just as in the rifle shooting and archery in sports and the microscope-related jobs requiring hand steadiness in industries. In anaerobic exercise condition hand steadiness is measured through hand steadiness tester having 9 different diameter holes after each subject exerts 25%, 50%, 75%, and 100% of maximum back strength. In aerobic exercise occasion it is evaluated at each time heart rate reaches 115%, 130%, and 145% of reference heart rate measured in no task condition after they do jumping jack. The results indicate that an increased intensity in both types of exercise reduces hand steadiness, but hand steadiness at 25% of maximum back strength and 115% of reference heart rate is rather greater than at no exercise. Just as the relation between cognitive stress and job performance has upside-down U form, so does the association of physical loading to hand steadiness, which means that a little exercise tends to improve hand steadiness in comparison with no exercise.

• PNF and Movement
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• v.12 no.4
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• pp.217-224
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• 2014

Purpose: The purpose of this study is to analyze the hand functions of elderly persons according to their cognitive status and age. Methods: A total of 65 persons voluntarily participated in the study. The subjects were divided into three groups: impairment cognitive group, normal cognitive group, adult group (persons in their twenties). Assessment of cognitive status was performed using a mini-mental state examination for Koreans (MMSE-K). Hand function was assessed using the Purdue pegboard test. The collected data were analyzed using a one-way ANOVA and Pearsonn Acorrelation. Results: There were significant differences in hand functions in the three groups. Post-hoc test results showed significant differences between each group. There were statistically significant differences in the correlation among hand functions, cognitive status, and age. The findings of this study suggest that hand functions have a positive correlation with cognitive status. However, a negative correlation was found between hand function and age. Conclusion: According to the study's results, hand functions are correlated with age and cognitive functions in elderly persons. This study suggests that hand rehabilitation with cognitive intervention increases hand functions in elderly persons.

• Journal of Trauma and Injury
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• v.34 no.4
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• pp.257-262
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• 2021

Purpose: Pediatric hand trauma is common and sometimes causes deformity or disability. The incidence and etiologies of hand trauma in children are different from those in adults. This study analyzed the characteristics of pediatric hand trauma cases and patients over a 15-year period. Methods: We conducted a retrospective medical record review of 3,432 children (2,265 boys, 1,167 girls, under 18 years of age) with hand injuries from January 2005 to December 2019. We evaluated the sex distribution and injury etiologies. Injuries were classified by type as burns, amputations, crushing injuries, lacerations, extensor and flexor tendon injuries, open and closed fractures, and nerve injuries. Results: Among the pediatric hand injury patients, males were predominant (1.94:1). Simple lacerations (58.4%) were the most common injury type, followed by fractures (22.8%). Lacerations and burns tended to be common in younger age groups, while tendon injuries, nerve injuries, and crushing injuries were more frequently encountered in older age groups. Conclusions: Hand trauma prevention strategies should be established considering the frequent trauma etiologies in specific age groups. An awareness of age-specific characteristics of pediatric hand trauma patients will be helpful to prevent hand trauma.

• 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.