• Journal of the Korean Society for Precision Engineering
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• v.17 no.6
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• pp.63-68
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• 2000

This paper describes the conceptual design of new robot hand by type synthesis. The conventional robot hands with rotational movement have bad controllability because the distance between the final rotational joint and the center of things grasped is changed according to the size of things grasped. This paper uses the systematic procedure of type synthesis and the knowledge for designing robot hands. The new 6-bar Watt I type conceptual robot hand with rectilinear translational movement is proposed.

• The Journal of Korean Physical Therapy
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• v.21 no.2
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• pp.9-14
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• 2009

Purpose: This study examined the effect of constraint-induced movement therapy (CIMT) on improving the hand function in hemiplegic side. Methods: Ten subjects without a control group were given CIMT to the hemiplegic side for 3 weeks. The effects of their hand function and sensibility were examined using a MAL and two point discrimination test. Repeated ANOVA was carried out for an analysis of the effects of the application of CIMT before and after treatment. Results: The participants showed significant improvement in their functional aspect with CIMT while there were no significant changes in the time domain variables. There was significant improvement in the quantitative and qualitative aspect of MAL, as well as significant improvement in the two-point discrimination function in all fingers. Conclusion: CIMT can enhance the motor function and sensory function of the hand in hemiplegic patients.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1770-1776
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• 2021

For hand expression recognition, hand pose recognition based on the static shape of the hand and hand gesture recognition based on hand movement are used together. In this paper, we proposed a hand expression recognition method that recognizes symbols based on the trajectory of a hand movement on a virtual blackboard. In order to recognize a sign drawn by hand on a virtual blackboard, not only a method of recognizing a sign from a hand movement, but also hand pose recognition for finding the start and end of data input is also required. In this paper, MediaPipe was used to recognize hand pose, and LSTM(Long Short Term Memory), a type of recurrent neural network, was used to recognize hand gesture from time series data. To verify the effectiveness of the proposed method, it was applied to the recognition of numbers written on a virtual blackboard, and a recognition rate of about 94% was obtained.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.219-221
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• 2007

Recent changes to ubiquitous computing requires more natural human-computer(HCI) interfaces that provide high information accessibility. Hand-gesture, i.e., gestures performed by one 'or two hands, is emerging as a viable technology to complement or replace conventional HCI technology. This paper deals with hand-posture recognition. Hand-posture database construction is important in hand-posture recognition. Human hand is composed of 27 bones and the movement of each joint is modeled by 23 degrees of freedom. Even for the same hand-posture,. grabbed images may differ depending on user's characteristic and relative position between the hand and cameras. To solve the difficulty in defining hand-postures and construct database effective in size, we present a method using a 3D hand model. Hand joint angles for each hand-posture and corresponding silhouette images from many viewpoints by projecting the model into image planes are used to construct the ?database. The proposed method does not require additional equations to define movement constraints of each joint. Also using the method, it is easy to get images of one hand-posture from many vi.ewpoints and distances. Hence it is possible to construct database more precisely and concretely. The validity of the method is evaluated by applying it to the hand-posture recognition system.

• The Journal of Korean society of community based occupational therapy
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• v.7 no.1
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• pp.25-35
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• 2017

Objective : The aims of this study was to investigate the effects of modified constraint induced movement therapy(CIMT) on upper extremities function of children with hemiplegic cerebral palsy. Methods : The participants of this study are 4 children with hemiplegic cerebral palsy aged between 8 to 11 years old. During the modified CIMT period, the unaffected hand of the subjects was restrained by a hand splint for 8 weeks, five days per week, five hours a day. And the affected upper extremity was strongly trained by performing functional tasks, which were individually structured use of the affected arm. Measurements used to assess hand function are Jebsen Hand Function Test, and 3D Motion Analysis. The Jebsen Hand Function Test was performed repeatedly every two weeks. The 3D Motion Analysis was performed before and after the 8 weeks of modified constraint induced movement therapy. Results : After the modified CIMT, there was a significant improvement in completed time for the 6 tasks of Jebsen Hand Function Test(p<.05). 3D Motion Analysis was that the finger tapping and the hand tapping has been significantly decreased (p<.05), and the pronation-supination movement has been significantly increased as well(p<.05). Conclusion : In the results of this study, it is evidenced that modified CIMT is effective treatment for upper extremities function of children with hemiplegic cerebral palsy. For future research, it is recommended to examine various periods and protocol of modified CIMT including impact of long periods application.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.164-170
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• 2004

The skin movement artifacts are referred to as the relative motion of skin with respect to the motion of underlying bones. This is of great importance in joint biomechanics or internal kinematics of human body. This paper describes a novel experiment that measures the skin movement of a hand based on MR(magnetic resonance) images in conjunction with surface modeling techniques. The proposed approach consists of 3 phases: (1) MR scanning of a hand with surface makers, (2) 3D reconstruction from the MR images, and (3) registration of the 3D models. The MR images of the hand are captured by 3 different postures. And the surface makers which are attached to the skin are employed to trace the skin motion. After reconstruction of 3D models from the scanned MR images, the global registration is applied to the 3D models based on the particular bone shape of different postures. The results of registration are then used to trace the skin movement by measuring the positions of the surface markers.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1106-1110
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• 2004

A human sign language is generally composed of both static and dynamic gestures. Each gesture is represented by a hand shape, its position, and hand movement (for a dynamic gesture). One of the problems found in automated sign language translation is on segmenting a hand movement that is part of a transitional movement from one hand gesture to another. This transitional gesture conveys no meaning, but serves as a connecting period between two consecutive gestures. Based on the observation that many dynamic gestures as appeared in Thai sign language dictionary are of quasi-periodic nature, a method was developed to differentiate between a (meaningful) dynamic gesture and a transitional movement. However, there are some meaningful dynamic gestures that are of non-periodic nature. Those gestures cannot be distinguished from a transitional movement by using the signal quasi-periodicity. This paper proposes a hybrid method using a combination of the periodicity-based gesture segmentation method with a HMM-based gesture classifier. The HMM classifier is used here to detect dynamic signs of non-periodic nature. Combined with the periodic-based gesture segmentation method, this hybrid scheme can be used to identify segments of a transitional movement. In addition, due to the use of quasi-periodic nature of many dynamic sign gestures, dimensionality of the HMM part of the proposed method is significantly reduced, resulting in computational saving as compared with a standard HMM-based method. Through experiment with real measurement, the proposed method's recognition performance is reported.

• Physical Therapy Korea
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• v.9 no.3
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• pp.67-76
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• 2002

We report the reorganization of motor network resulted from intensive unilateral coordination training and the effect of cross education on the untrained side in patient with traumatic brain injury using functional magnetic resonance imaging (fMRI). A 22 year-old male patient who had suffered from diffuse axonal injury for 58 months showed coordination deficit in the left hand at initial examination. Intensive motor training including complex finger movements and coordination activities using a metronome was introduced to the patient 4 hours per day for a week. FMRI was performed on a 3T ISOL Forte scanner. All functional images were analyzed using SPM-99 software. Hand function was improved after training not only in the trained left hand, but also in the untrained right hand. There was no activation in the right primary motor area (M1) during left hand movement before training whereas robust activation of left M1 was demonstrated by the right hand movement. Profuse activation of bilateral prefrontal lobes was seen during both hand movements before training. After training of left hand, right M1 became prominently activated during the left hand motion. The activation of bilateral prefrontal lobes disappeared after training not only for the left hand movement but also for the right, which clearly demonstrated the effect of cross education. This case report demonstrated the learning-dependent reorganization of the M1 and the effect of cross education.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1377-1383
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• 2012

This paper proposes a human mouse system that replaces mouse input by human hand movement. As the resolution of monitors increases, it is not quite possible, due to the resolution difference between web cameras and monitors, to place the cursor in the entire range of a monitor by simply moving the pointer which recognizes the position of the hand from the web camera. In this regard, we propose an effective method of placing the position of the mouse, without repeating the returning hand movements, in the corners of the monitor in which the user wants it to be. We also proposes the recognition method of finger movements in terms of using thumb and index finger. The measurement that we conducted shows the successful recognition rate of 97% that corroborates the effectiveness of our method.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.64-70
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• 2014

In this paper, we propose a method that gives motion command to a mobile robot to recognize human being's hand gesture. Former way of the robot-controlling system with the movement of hand used several kinds of pre-arranged gesture, therefore the ordering motion was unnatural. Also it forced people to study the pre-arranged gesture, making it more inconvenient. To solve this problem, there are many researches going on trying to figure out another way to make the machine to recognize the movement of the hand. In this paper, we used third-dimensional camera to obtain the color and depth data, which can be used to search the human hand and recognize its movement based on it. We used HMM method to make the proposed system to perceive the movement, then the observed data transfers to the robot making it to move at the direction where we want it to be.