• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• The Journal of Korean Society for School & Community Health Education
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• v.9 no.2
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• pp.81-90
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• 2008

Objectives: Since 80% of the information we get from the environment comes in through our eyes (Anshel JR, 1999), uncorrected visual problems negatively affect children's educational process and perceptual development. The objectives of this study were: 1st, to document the prevalence of learning related vision problem in primary school children. 2nd, to compare responses of children with those of parents on visual symptoms. Lastly, to determine if there is an association between visual symptoms and academic performance. Methods: We administered visual-symptom quality of life questionnaire developed by Oklahoma College of Optometry in Vision Development to 1031 primary school children and their parent. Visual symptoms responded by children and their parents were compared using Independent Sample t-test and the relation between visual symptoms and academic performance were calculated using Pearson Correlation tests. Results and Conclusions: The number of children who need further professional evaluation, that is visual-symptom scores were ${\geq}20$, reported by children(25%) was greater than that reported by parents(16%). And visual-symptom scores reported by children were significantly higher than those reported by parents in every grade(p<0.01, p<0.001). Visual symptoms reported by both children and parents were found to be inversely correlated to academic performance in every academic area and most of their correlations were statistically significant(p<0.05). Therefore, children with more visual-symptom reported by both group had negative effects on children's academic performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.881-895
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• 2023

Modern image inpainting techniques based on deep learning have achieved remarkable performance, and more and more people are working on repairing more complex and larger missing areas, although this is still challenging, especially for facial image inpainting. For a face image with a huge missing area, there are very few valid pixels available; however, people have an ability to imagine the complete picture in their mind according to their subjective will. It is important to simulate this capability while maintaining the identity features of the face as much as possible. To achieve this goal, we propose a three-stage network model, which we refer to as the identity and structure feature refinement network (ISFRNet). ISFRNet is based on 1) a pre-trained pSp-styleGAN model that generates an extremely realistic face image with rich structural features; 2) a shallow structured network with a small receptive field; and 3) a modified U-net with two encoders and a decoder, which has a large receptive field. We choose structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), L1 Loss and learned perceptual image patch similarity (LPIPS) to evaluate our model. When the missing region is 20%-40%, the above four metric scores of our model are 28.12, 0.942, 0.015 and 0.090, respectively. When the lost area is between 40% and 60%, the metric scores are 23.31, 0.840, 0.053 and 0.177, respectively. Our inpainting network not only guarantees excellent face identity feature recovery but also exhibits state-of-the-art performance compared to other multi-stage refinement models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.980-997
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• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.448-463
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• 2017

Multimedia is a ubiquitous and indispensable part of our daily life and learning such as audio, image, and video. Objective and subjective quality evaluations play an important role in various multimedia applications. Blind image quality assessment (BIQA) is used to indicate the perceptual quality of a distorted image, while its reference image is not considered and used. Blur is one of the common image distortions. In this paper, we propose a novel BIQA index for Gaussian blur distortion based on the fact that images with different blur degree will have different changes through the same blur. We describe this discrimination from three aspects: color, edge, and structure. For color, we adopt color histogram; for edge, we use edge intensity map, and saliency map is used as the weighting function to be consistent with human visual system (HVS); for structure, we use structure tensor and structural similarity (SSIM) index. Numerous experiments based on four benchmark databases show that our proposed index is highly consistent with the subjective quality assessment.

• Korean Journal of Applied Biomechanics
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• v.12 no.2
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• pp.407-415
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• 2002

Athletic products must meet the needs of athletes and the demands imposed by sports through innovative design. These needs of athletes and requirements of sports are performance, protection and comfort related. In depth knowledge of anatomy and physiology, etiology of commonly reported injuries, and lower extremity mechanics form the basis of product creation/engineering. Game analysis which entails time and frequency surveys of the skills performed during a game, interviews with athletes and coaches, and discussions with medical staffs are used to identify the skills that are critical to the needs of athletes. In lab full biomechanical analyses of these skills and/or physiological responses of the athletes lead to clear functional criterions that serve as guidelines to be met by the design team. The concepts created by the design team are in turns subjected to the same battery of biomechanical analyses. The learning gathered through this pluridisciplinary process is used to further evolve design concepts. The evolution-testing loop is repeated until biomechanical and/or physiological, mechanical and perceptual tests indicate that the design concept meets the established functional design criterions. At that time, the design concepts is ready for manufacturing research and development. Additional biomechanical and physical tests are performed through that phase to confirm that the manufacturing processes preserve the functionality of the design concept. Durability and long term performance of production samples are evaluated through a final three month long weartest program. A rigorous research/testing program is crucial to create and engineer sport products that meet the performance, protection.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2005.05a
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• pp.154-157
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• 2005

본 연구는 한국어를 학습한 경험이 없는 일본인의 한국어 치경폐쇄음 세 음소 범주(/ㄷ/, /ㄸ/, /ㅌ/)에 대한 지각 학습이 양순폐쇄음 세 음소(/ㅂ/. /ㅃ/, /ㅍ/) 지각에 미치는 효과를 검토하였다. 김윤현과 김정오 (2005)는 일본인들이 지각 학습과제에서 한국어 치경폐쇄음 세 범주를 구분할 때 기식성. 긴장성과 같은 변별 자질에 선택주의 하게 됨을 시사하는 결과를 얻었다. 치경음에 대한 지각 학습으로 적절한 단서에 선택주의 하게 되었다면, 같은 지각 차원에 따라 세 범주로 구분되는 양순음의 경우에도 치경폐쇄음 학습 후 음성자극들을 옳게 범주화를 할 것이다. 실험 결과, 치경폐쇄음 자극(/다/, /따/, /타/)만으로 이루어진 동일-상이판단 학습 과제에서 치경폐쇄음 파악의 정확율은 29.1%(표준오차=3.02) 증가하였고, 조음 위치의 변화에 따른 음향적 차이에도 불구하고 양순폐쇄음의 정반응율도 15.8%(표준오차=3.27)의 향상을 보였다. 이 전이효과는 치경음 지각 학습 때문에 일본인들이 폐쇄음의 세 음소 범주를 구분하는 적절한 지각 차원에 선택주의하게 되었음을 시사한다.

• Korean Journal of Cognitive Science
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• v.15 no.3
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• pp.1-14
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• 2004

This research examined the effects of individual differences on visual discrimination skill teaming and its transfer to novel stimuli. Individual participants were categorized as having an analytic or holistic cognitive style, high or low working memory capacity, and high or low levels of rationality, experientiality, and adaptive decision-making styles. Participants received easy or difficult training for the serially presented discrimination task, and then transferred to novel discriminations. Training content interacted with cognitive style and working memory capacity to affect transfer accuracy performance, but individual differences in decision-making styles did not affect transfer performance. Results suggest individual differences should be taken into account when designing an interface for visual discrimination.

• The Journal of Korean Academic Society of Nursing Education
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• v.12 no.2
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• pp.229-237
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• 2006

Purpose: The purpose of this study was to investigate the influencing factors of interpersonal relationship in nursing college students for effective learning ability and teaching strategy. Method: In order to get the data by self-questionnaire, 166 subjects were selected. The instruments for this study were Preceptual Orientation Scale, Self-Efficacy Scale, Narcissistic Personality Disorder Scale, and Interpersonal Relationship Scale. The dada was analyzed by percentage, mean, standard deviation, t-test, one-way ANOVA, Scheffe' test, Pearson's correlation coefficient and Stepwise multiple regression using SPSS 12.0 program. Result: The main factors that affect interpersonal relationship were self-perception and social-efficacy. These variables were account for 37.9% of interpersonal relationship. The significant influencing factors on interpersonal relationship were self-perception, social-efficacy. Conclusion: It is necessary to develop a strategy to get positive perceptual orientation and successful interpersonal relationship for nursing college students by further studies with small group program for the best result.

• International Journal of Knowledge Content Development & Technology
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• v.6 no.2
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• pp.43-66
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• 2016

The primary objective of this study was to investigate the perception of informational professionals' knowledge sharing practices in social media platforms. The specific objectives of the study included learning professionals' perceptions and awareness of knowledge sharing using social media, understanding their opinions and beliefs, and gaining familiarity with and reasons for using these tools. Open & close ended web-based questions were sent out by email to the international training program (ITP) participants. Findings indicated that most of the respondents' were aware of using social media and that they used social media for knowledge sharing. Speed and ease of use, managing personal knowledge, easier communication with users and colleagues and powerful communication tool are the areas that motivated them to use it. It also stated some barriers like lack of support, familiarity, trust, unfiltered information and fear of providing information. The study was limited to the perceptual aspect of the issue, specifically from the individuals' opinions and sentiments.