• Journal of Korean Academy of Nursing
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• v.33 no.5
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• pp.591-600
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• 2003

Purpose: The purpose of this study was to investigate the effects of exercise training using biofeedback and constraint-induced movement on the upper extremity function of hemiplegic patients. Method: A non-equivalent pretest-posttest design was used. Study subjects were a conveniently selected group of 40 hemiplegic patients(20 experimental subjects, 20 control subjects) who have been enrolled in two community health centers. After biofeedback training the subjects of experimental group were given constraint-induced movement, involving restraint of unaffected U/E in a sling for about 6 hours in a day over a period of two weeks, while at the same time intensively training the affected U/E. Outcomes were evaluated on the basis of the VIE motor ability(hand function, grip power, pinch power, U/E ROMs), and motor activity(amount, quality). Result: 1. After 2 weeks of treatment, the motor abilities of affected U/E(hand function, grip power, pinch power, ROMs of wrist flexion, elbow flexion and shoulder flexion/extension) were significantly higher in subjects who participated in exercise training than in subjects in the control group with no decrement at 4-week follow-up. However, there was no significant difference in wrist extension between experimental or control group. 2. After 2 weeks of treatment, the amount of use and the quality of motor activity of affected U/E were significantly higher in subjects who participated in exercise training than in subjects in the control group with no decrement at 4-week follow-up. Conclusion: The above results state that exercise training using biofeedback and constraint-induced movement could be an effective intervention for improving U/E function of chronic hemiplegic patients. Long-term studies are needed to determine the lasting effects of constraint-induced movement.

• Journal of the Ergonomics Society of Korea
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• v.34 no.1
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• pp.19-27
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• 2015

Objective: The aim of this study is to investigate how well eye movement times in visual target selection tasks by an eye input device follows the typical Fitts' Law and to compare vertical and horizontal eye movement times. Background: Typically manual pointing provides excellent fit to the Fitts' Law model. However, when an eye input device is used for the visual target selection tasks, there were some debates on whether the eye movement times in can be described by the Fitts' Law. More empirical studies should be added to resolve these debates. This study is an empirical study for resolving this debate. On the other hand, many researchers reported the direction of movement in typical manual pointing has some effects on the movement times. The other question in this study is whether the direction of eye movement also affects the eye movement times. Method: A cursor movement times in visual target selection tasks by both input devices were collected. The layout of visual targets was set up by two types. Cursor starting position for vertical movement times were in the top of the monitor and visual targets were located in the bottom, while cursor starting positions for horizontal movement times were in the right of the monitor and visual targets were located in the left. Results: Although eye movement time was described by the Fitts' Law, the error rate was high and correlation was relatively low ($R^2=0.80$ for horizontal movements and $R^2=0.66$ for vertical movements), compared to those of manual movement. According to the movement direction, manual movement times were not significantly different, but eye movement times were significantly different. Conclusion: Eye movement times in the selection of visual targets by an eye-gaze input device could be described and predicted by the Fitts' Law. Eye movement times were significantly different according to the direction of eye movement. Application: The results of this study might help to understand eye movement times in visual target selection tasks by the eye input devices.

• Journal of the Korea Computer Industry Society
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• v.7 no.3
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• pp.253-262
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• 2006

There are some differences between the movements that are produced with closed system and opened system. When an object is moved by the force occurred inside the object, It is called closed movement on the other hand,when the object is moved by the external force. the system is called opened movement. The closed movement model is consist of a linear closed movement system and a nonlinear closed movement system. The approximate equations of the approximate model are derived from the principles and experimental devices of the linear closed movement systems. Various nonlinear closed movement modes and experimental devices are also compared. The results show that linear closed movement model can be derived from nonlinear system due to the couple of nonlinear closed movement model.

• Journal of Korea Game Society
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• v.16 no.6
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• pp.49-58
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• 2016

This paper proposes three schemes of one-hand control that can be used in the First-Person Viewpoint mobile games and evaluates them. Displaying virtual controllers on the screen, the existing mobile games use schema that allows users use both hands. In the First-Person Viewpoint mobile games, however, one-hand control is possible because smaller number of functions for viewpoint movement is required. This research proposes three one-hand control schemes for First-Person Viewpoint movement and rotation, and evaluates them using AHP. The experiment results prove that one-hand control schemes are more useful than both-hands control schemes for the mobile game users.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.425-438
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• 2012

In this paper, We propose Augmented Reality (AR) System for the interaction between user's hand motion and virtual object motion based on computer vision. The previous AR system provides inconvenience to user because the users have to control the marker and the sensor like a tracker. We solved the problem through hand motion and provide the convenience to the user. Also the motion of virtual object using a physical phenomenon gives a reality. The proposed system obtains geometrical information by the marker and hand. The system environments like virtual space of moving virtual ball and bricks are made by using the geometrical information and user's hand motion is obtained from the hand's information with extracted feature point through the taping hand. And it registers a virtual plane stably by getting movement of the feature points. The movement of the virtual ball basically is parabolic motion with a parabolic equation. When the collision occurs either the planes or the bricks, we show movement of the virtual ball with ball position and normal vector of plane and the ball position is faulted. So we showed corrected ball position through experiment. and we proved that this system can replaced the marker system to compare to jitter of augmented virtual object and progress speed with it.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3971-3975
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• 2013

This paper introduces an Augmented Reality system recognizing hand gestures and shows results of the evaluation. The system's user can interact with artificial objects and manipulate their position and motions simply by his hand gestures. Hand gesture recognition is based on Histograms of Oriented Gradients (HOG). Salient features of human hand appearance are detected by HOG blocks. Blocks of different sizes are tested to define the most suitable configuration. To select the most informative blocks for classification multiclass AdaBoostSVM algorithm is applied. Evaluated recognition rate of the algorithm is 94.0%.

• Journal of Korean Physical Therapy Science
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• v.9 no.2
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• pp.133-141
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• 2002

This purpose of this study was to design the effect of recovering of a hand amputees by Myoelectronic hand. It was designed with 2 degree of freedom in the laboratory. Myoelectronic hand had only one degree of freedom and one movement until now. Also this myoelectronic hand had multi-joint and it could move widely. Wire was used in transmission. Myoelectronic hand data was obtained by analyzing hand anatomically and measuring and that data was applied when it was designed. PID controller of Myoelectronic hand was used to it. Displacement control was applied the first link. Experiment was accomplished in Tip grasp, Power grasp and Hook grasp modes. Displacement control was good in low frequency. Velocity control was applied to each mode. The objective of the study was to develop more better multifunction myoelectronic control strategies. A myoelectronic hand with a hand amputees could do some jobs such as grasping materials, lifting weighting, holding cup and etc. As a result of this study, all subjects with hand amputees significantly improved in ADL. Further studies were needed to evaluate the effect of a myoelectronic hand with more precise laboratory equipment.

• The korean journal of orthodontics
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• v.33 no.5 s.100
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• pp.399-405
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• 2003

Tooth movement facilitated by corticotomy and distraction osteogenesis, new paradigm in orthodontics, was discussed. Intrusive tooth movement of anterior or posterior teeth was thought to be difficult or impossible. In this study, a part of cortical bone, which was a sort of resistance to tooth movement in alveolar bone, was removed. On the other hand, active bone deposition was made possible in the tension side. That was the main concept of tooth movement facilitated by corticotomy and distraction osteogenesis. Teeth moved at such a speedy tate as we could not imagine in conventional tooth movement, which lead to the reduction of total treatment Period. And intrusive movement was Possible without a side effect, lot example, root resorption or the periodontal breakdown. Those were the superior aspects to the conventional orthodontics.

• Science of Emotion and Sensibility
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• v.9 no.2
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• pp.119-132
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• 2006

The dimensions of affection structure from hand movements was developed for the purpose of understanding relationship between affective words and physical factors to apply it to computing environment. To analyze hand movements, three dimensions -direction, time, weight- were found through reconstructing sub-properties of Laban Movement Analysis. The direction dimension has five freedoms of movement (horizontal, vertical, sagittal, circular, shaking) while the time and weight dimensions both have two sub categories each, (sudden, sustained), (light, strong) respectively. By factorial design using the three dimensions, twenty movement were videotaped. Participants rated a list of fifty korean affective words on each twenty movements. The results were studied by nonlinear principal component analysis. The results suggested that time and weight dimensions are closely related with arousal level dimension of affection. Strong and sudden movements associated with highly aroused affection, while light and sustained movements associated with the opposite affection. The direction sub-dimensions were found to be associated with the kinds of affection. Linear movements like horizontal, vortical and sagittal direction were correlated to highly aroused negative affection. Circular movements were found to correlate closely by fun and delight on the graph, while shaking movements were correlated to anxiety and impatience. These results imply that the dimensions of affection structure and sub-properties of hand movements are closely connected with each other.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.19-23
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• 2024

Patients with amputations of both hands need prosthetic hands that serve both cosmetic and functional purposes, and research on prosthetic hands using electromyography of remaining muscles is active, but there is still the problem of high cost. In this study, an artificial prosthetic hand was manufactured and its performance was evaluated using low-cost parts and software such as a surface electromyography sensor, machine learning software Edge Impulse, Arduino Nano 33 BLE, and 3D printing. Using signals acquired with surface electromyography sensors and subjected to digital signal processing through Edge Impulse, the flexing movement signals of each finger were transmitted to the fingers of the prosthetic hand model through training to determine the type of finger movement using machine learning. When the digital signal processing conditions were set to a notch filter of 60 Hz, a bandpass filter of 10-300 Hz, and a sampling frequency of 1,000 Hz, the accuracy of machine learning was the highest at 82.1%. The possibility of being confused between each finger flexion movement was highest for the ring finger, with a 44.7% chance of being confused with the movement of the index finger. More research is needed to successfully develop a low-cost prosthetic hand.