• 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.

• 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.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.85-93
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• 2005

In this paper, we propose recognition system which tracks the unmarked hand of a person performing sign language in complex background. First of all, we measure entropy for the difference image between continuous frames. Using a color information that is similar to a skin color in candidate region which has high value, we extract hand region only from background image. On the extracted hand region, we detect a contour and recognize sign language by applying improved centroidal profile method. In the experimental results for 6 kinds of sing language movement, unlike existing methods, we can stably recognize sign language in complex background and illumination changes without marker. Also, it shows the recognition rate with more than 95% for person and $90\sim100%$ for each movement at 15 frames/second.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.9
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• pp.833-839
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• 2010

In this paper, a novel 1 DOF hand rehabilitation robot is proposed in consideration of ADL training for stroke patients. To perform several ADL trainings, the proposed robot can move the thumb part and the part of 4 fingers simultaneously and realize the full ROM (Range of Motion) in grasp. Based on these characteristics, the proposed robot realizes several types of grasp such as cylindrical grasp, lateral grasp, and pinch grasp by using a passive revolute joint that can change the thumb movement direction. The movement of the thumb is driven by a cable mechanism and the part of 4 fingers is moved by a four-bar linkage mechanism.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.299-307
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• 2022

In this paper, we propose a pre-processing method that converts to Spider Chart image data for classification of gripping movement using EMG (electromyography) sensors and Convolution Neural Networks (CNN) deep learning. First, raw data for six hand gestures are extracted from five test subjects using an 8-channel armband and converted into Spider Chart data of octagonal shapes, which are divided into several sliding windows and are learned. In classifying six hand gestures, the classification performance is compared with the proposed pre-processing method and the existing methods. Deep learning was performed on the dataset by dividing 70% of the total into training, 15% as testing, and 15% as validation. For system performance evaluation, five cross-validations were applied by dividing 80% of the entire dataset by training and 20% by testing. The proposed method generates 97% and 94.54% in cross-validation and general tests, respectively, using the Spider Chart preprocessing, which was better results than the conventional methods.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.501-507
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• 2012

Human body motion is a non-verbal part for interaction or movement that can be used to involves real world and virtual world. In this paper, we explain a study on natural user interface (NUI) in human hand motion recognition using RGB color information and depth information by Kinect camera from Microsoft Corporation. To achieve the goal, hand tracking and gesture recognition have no major dependencies of the work environment, lighting or users' skin color, libraries of particular use for natural interaction and Kinect device, which serves to provide RGB images of the environment and the depth map of the scene were used. An improved Camshift tracking algorithm is used to tracking hand motion, the experimental results show out it has better performance than Camshift algorithm, and it has higher stability and accuracy as well.

• Physical Therapy Korea
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• v.28 no.3
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• pp.208-214
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• 2021

Background: The CKCUES test evaluates the functional performance of the shoulder joint. The CKCUES test scores CKC exercises of the upper limbs to examine shoulder stability. Although the CKCUES test provides quantitative data on functional ability and performance, no study has determined the relationship between CKCUES scores and SA and TB muscle strength. Objects: The objective of this study is to determine the relationship between the CKCUES test scores and the strength of the SA and TB muscles in the CKCUES and unilateral CKCUES tests. Methods: Sixty-six healthy male volunteers participated in the study. A Smart KEMA strength sensor measured SA and TB muscle strength. Two parallel lines on the floor indicated the initial hand placement to start CKCUES tests. For 15 seconds, the subject raises one hand and reaches over to touch the supporting hand, then returns to the starting position. Results: The correlation between the CKCUES test scores and the strength of the SA was strong (r = 0.650, p < 0.001), and the TB was moderate (r = 0.438, p < 0.001). The correlation between the unilateral CKCUES test and the strength of the SA of the supporting side was strong (r = 0.605, p < 0.001), and swing side was strong (r = 0.681, p < 0.001). The correlation between the unilateral CKCUES test and the strength of the TB of the supporting side was moderate (r = 0.409, p < 0.001), and swing side was moderate (r = 0.482, p < 0.001). Conclusion: Our study showed that the CKCUES test had a strong association with isometric strength of SA and moderate association with that of TB. These findings suggest that the CKCUES test can evaluate the function of the SA. Moreover, the unilateral CKCUES test can evaluate unilateral shoulder function.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.1
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• pp.68-72
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• 2015

Ultrasonic inspection robot systems have been widely researched and developed for the real-time monitoring of structures such as power plants. However, an inspection robot that is operated in a simple pattern has limitations in its application to various structures in a plant facility because of the diverse and complicated shapes of the inspection objects. Therefore, accurate control of the robot is required to inspect complicated objects with high-precision results. This paper presents the idea that the shape and movement information of an ultrasonic inspector's hand could be profitably utilized for the accurate control of robot. In this study, a polymer flex sensor was applied to monitor the shape of a human hand. This application was designed to intuitively control an ultrasonic inspection robot. The movement and shape of the hand were estimated by applying multiple sensors. Moreover, it was successfully shown that a test robot could be intuitively controlled based on the shape of a human hand estimated using polymer flex sensors.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.51-57
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• 2021

In this paper, we propose a new framework that calculates the user's hand motions using a Leap Motion device, and uses this to practice and analyze arm muscles as well as juggling motions. The proposed method can map the movement of the ball in a virtual environment according to the user's hand motions in real time, and analyze the amount of exercise by visualizing the relaxation and contraction of the muscles. The proposed framework consists of three main parts : 1) It tracks the user's hand position with the Leap Motion device. 2) As with juggling, the action pattern of the user throwing the ball is defined as an event. 3) We propose a parabola-based particle method to map the movement of a juggling shape to a ball based on the user's hand position. As a result, using the our framework, it is possible to play a juggling game in real-time.

• Journal of Sport and Applied Science
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• v.7 no.2
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• pp.39-51
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• 2023

Purpose: The purpose of this study was to investigate differences in the control process to satisfy spatial and temporal constraints imposed upon the anticipation timing response by analyzing the effect of spatio-temporal accuracy demands on eye movements, response accuracy, and the coupling of eye and hand movements. Research design, data, and methodology: 12 right-handed male subjects participated in the experiment and performed anticipation timing responses toward a stimulus moving at three velocities (0.53m/s, 0.66m/s, 0.88m/s) in two task constraint conditions (temporal constraint, spatial constraint). During the response, response accuracy and eye movement patterns were measured from which timing and radial errors, the latency of saccade, fixation duration of the point of gaze (POG), distance between the POG and stimulus, and spatio-temporal coupling of the POG and hand were calculated. Results: The timing and radial errors increased with increasing stimulus velocity, and the spatio-temporal constraints led to larger timing errors than the temporal constraints. The latency of saccade and the temporal coupling of eye and hand decreased with increasing stimulus velocity and were shorter and longer respectively in the spatio-temporal constraint condition than in the temporal constraint condition. The fixation duration of the POG also decreased with increasing stimulus velocity, but no difference was shown between task constraint conditions. The distance between the POG and stimulus increased with increasing stimulus velocity and was longer in the temporal constraint condition compared to the spatio-temporal constraint condition. The spatial coupling of eye and hand was larger with the velocity 0.88m/s than those in other velocity conditions. Conclusions: These results suggest that differences in eye movement patterns and spatio-temporal couplings of stimulus, eye and hand by task constraints are closely related with the accuracy of anticipation timing responses, and the spatial constraints imposed may decrease the temporal accuracy of response by increasing the complexity of perception-action coupling.