• The Journal of Korean Physical Therapy
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• v.22 no.4
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• pp.91-96
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• 2010

Purpose: Among computer users, the awkward posture and workstation setups of workers contribute to work-related upper limb musculoskeletal disorders. The purpose of this study was to evaluate the efficacy of a traditional physical therapy and ergonomic intervention by physical therapists in 4 computer users. Methods: After checking Visual Analogue Scores (VAS), four subjects who were treated by physical therapy for neck and shoulder problems related to VDT syndrome were enrolled in the study. All subjects spent at least 40 hours per week at a computer workstation. All subjects had pain and ergonomic states evaluated using methods such as VAS, Neck Disability Index (NDI), Workstyle short form, Rapid Upper Limb Assessment (RULA), and OSHA VDT checklist (Occupational Safety and Health Administration video display terminal) before a physical therapy + ergonomic intervention. Participants were re-evaluated 1 month later. Results: Participants showed more improvement of their neck pain after being treated with a physical therapy plus ergonomic intervention than when treatment consisted only of physical therapy. Improvements in RULA, Workstyle short form, and OSHA VDT checklist also were achieved. Conclusion: This case study suggests the importance of examining the work habits and work-related postures of subjects who complain of neck and shoulder pain that is exacerbated by computer use. Personalized ergonomic interventions and physical therapy can lead to improvement of patients with VDT syndrome.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3599-3619
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• 2019

In human activity recognition system both static and motion information play crucial role for efficient and competitive results. Most of the existing methods are insufficient to extract video features and unable to investigate the level of contribution of both (Static and Motion) components. Our work highlights this problem and proposes Static-Motion fused features descriptor (SMFD), which intelligently leverages both static and motion features in the form of descriptor. First, static features are learned by two-stream 3D convolutional neural network. Second, trajectories are extracted by tracking key points and only those trajectories have been selected which are located in central region of the original video frame in order to to reduce irrelevant background trajectories as well computational complexity. Then, shape and motion descriptors are obtained along with key points by using SIFT flow. Next, cholesky transformation is introduced to fuse static and motion feature vectors to guarantee the equal contribution of all descriptors. Finally, Long Short-Term Memory (LSTM) network is utilized to discover long-term temporal dependencies and final prediction. To confirm the effectiveness of the proposed approach, extensive experiments have been conducted on three well-known datasets i.e. UCF101, HMDB51 and YouTube. Findings shows that the resulting recognition system is on par with state-of-the-art methods.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.138-144
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• 2018

A video is a vivid medium similar to human's visual-linguistic experiences, since it can inculcate a sequence of situations, actions or dialogues that can be told as a story. In this study, we propose story learning/regeneration frameworks from videos with successive event order supervision for contextual coherence. The supervision induces each episode to have a form of trajectory in the latent space, which constructs a composite representation of ordering and semantics. In this study, we incorporated the use of kids videos as a training data. Some of the advantages associated with the kids videos include omnibus style, simple/explicit storyline in short, chronological narrative order, and relatively limited number of characters and spatial environments. We build the encoder-decoder structure with successive event order embedding, and train bi-directional LSTMs as sequence models considering multi-step sequence prediction. Using a series of approximately 200 episodes of kids videos named 'Pororo the Little Penguin', we give empirical results for story regeneration tasks and SEOE. In addition, each episode shows a trajectory-like shape on the latent space of the model, which gives the geometric information for the sequence models.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.62-69
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• 2013

This paper proposes real-time video transmission system by the car-mounted cameras based on MOST Network. Existing vehicles transmit videos by connecting the car-mounted cameras in the form of analog. However, the increase in the number of car-mounted cameras leads to development of the network to connect the cameras. In this paper, DSP is applied to process MPEG 2 encoding/decoding for real-time video transmission in a short period of time. MediaLB is employed to transfer data stream between DSP and MOST network controller. During this procedure, DSP cannot transport data stream directly from MediaLB. Therefore, FPGA is used to deliver data stream transmitting MediaLB to DSP. MediaLB is designed to streamline hardware/software application development for MOST Network and to support all MOST Network data transportation methods. As seen in this paper, the test results verify that real-time video transmission using proposed system operates in a normal matter.

• Journal of Industrial Convergence
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• v.21 no.8
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• pp.13-21
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• 2023

The purpose of this study was to compare storytelling with TV entertainment programs to find out which elements of web entertainment affected the shift of viewers' interest, and to identify the characteristics of web entertainment storytelling. To this end, each of the web and TV entertainment programs were selected for storytelling analysis, and storytelling analyzed the contents of each item by dividing them into images, backgrounds, stories, and characters. As a result of the analysis, unlike TV programs, web entertainment storytelling allows viewers to immerse themselves in content through a composition that runs directly from the beginning to the crisis, and is characterized by a clear formation in a short video through a clear ending narrative. These research results hope that short-form web entertainment programs produced in the future will be able to identify strategies for viewers' immersion and storytelling strategies.

• The Journal of Information Systems
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• v.32 no.2
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• pp.177-190
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• 2023

Purpose Recently, as a new business marketing tool, short form content focused on fun and interest has been shared as hashtags. By extracting positive and negative keywords from media audiences through comment analysis of social media news, various stakeholders aim to quickly and easily grasp users' opinions on major news. Design/methodology/approach YouTube videos were searched using the YouTube Data API and the results were collected. Video comments were crawled and implemented as HTML elements, and the collection results were checked on the web page. The collected data consisted of video thumbnails, titles, contents, and comments. Comments were word tokenized with the R program, comparing positive and negative dictionaries, and then quantifying polarity. In addition, social network analysis was conducted using divided positive and negative comments, and the results of centrality analysis and visualization were confirmed. Findings Social media users' opinions on issue news were confirmed by analyzing and visualizing the centrality of keywords through social network analysis by dividing comments into positive and negative. As a result of the analysis, it was found that negative objective reviews had the highest effect on information usefulness. In this way, previous studies have been reaffirmed that online negative information has a strong effect on personal decision-making. Corporate marketers will analyze user comments on social network services (SNS) to detect negative opinions about products or corporate images, which will serve as an opportunity to satisfy customers' needs.

• Journal of the Korean Society of Costume
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• v.59 no.2
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• pp.154-168
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• 2009

This study aims on providing a design technique that expresses aesthetical elements by arranging the analysis of sensual beauty into detailed elements of design from music video outfits by genre of pop music, by observing music videos of female vocalists chosen from each genre of pop music focusing on their fashion. The results of this study are the following. 1. In the genre rock, the sensual beauty of female were expressed with a boyish and neutral style using texture such as leather or denim, and such style had the effect of emphasizing their feminine side even more. 2. In the genre dance music, exposure is extensive compared to other genre using sexy or lingerie look, and I found an ambivalent style of feminism with clothes in the form of drapery using textures such as chiffon and silk, and femme fatale style with textures adhering to the body such as leggings, leotard, and bodysuit. 3. In the genre of rap and hip-hop, clothes from casual and costume-play style were found using training jersey, t-shirt, and denim pants, and emphasized the sensual beauty of women by showing a silhouette with short length and fitting style using shiny textures. 4. In the genre of R&B, there were diverse outfits that suits the characteristics of characters appearing in the stories, or the situation of the story since there are many dramatic representation in the form of story Especially in case of female characters, the feminine side was emphasized staging a feminine style by wearing dresses with the texture of chiffon and silk. Exposure was restrained compared to other genre.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.91-94
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• 2021

As deep learning technologies becoming developed, realistic fake videos synthesized by deep learning models called "Deepfake" videos became even more difficult to distinguish from original videos. As fake news or Deepfake blackmailing are causing confusion and serious problems, this paper suggests a novel model detecting Deepfake videos. We chose Residual Convolutional Neural Network (Resnet50) as an extraction model and Long Short-Term Memory (LSTM) which is a form of Recurrent Neural Network (RNN) as a classification model. We adopted cosine similarity with hinge loss to train our extraction model in embedding the features of Deepfake and original video. The result in this paper demonstrates that temporal features in the videos are essential for detecting Deepfake videos.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.125-132
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• 2021

This study deals with the influencing factors on flow and addiction perceived by users of Tiktok service, an SFV service platform that is expanding the market in the middle area between social media and OTT. As the number of Tiktok users increases, researchers thought that research on the cause of addiction would be necessary. Since media users lack media consumption time, they produce and share SFVs rather than long videos, and are affected by exogenous variables. In addition, attachment is divided into interpersonal relationships and attachment to services, and the path of attachment was confirmed to be connected to flow and addiction. Through this study, the researchers considered that there were theoretical and practical contributions in that the path leading to addiction of video media services was set and verified as self-exposure and attachment, flow and addiction. These research results can be applied to more diversified video-centered media services, and can be expected to be used for new media emerging in the future.

• Journal of Nutrition and Health
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• v.46 no.4
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• pp.357-368
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• 2013

Tailored nutritional education is generally found to be more effective in changing behaviors and to be more fully implemented than a non-tailored equivalent. This study was conducted in order to develop tailored nutritional education materials on food additives in processed foods based on need and levels of knowledge of educational targets of elementary-school students in Seoul Metropolitan City. The focus group interview was conducted with six elementary-school nutrition teachers in order to gather information and to develop a tailored quantitative questionnaire for the survey. Based on the results from 138 nutrition teachers, all answered that education on food additives in processed foods for students is necessary and both teachers and students need to receive education regarding definition, safety, and use of food additives for each processed food, in the form of video, PPT, and teaching-learning plan. Nutritional education materials for two classes were developed using video clips (grocery shopping and cooking class) about food additives in processed foods, PPTs with activity papers, two teaching-learning plans, and school newsletters to parents. In conclusion, the current study warrants conduct of further studies short-term and long-term impacts and efficacy of tailored need-based nutrition education in promotion of healthy nutrition by conveying proper scientific knowledge regarding food additives in processed foods for elementary-school students.