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- P-ISSN1738-6764
- E-ISSN2093-7504
- KCI
ISSN : 1738-6764
Vol.16 No.3
Abstract
Human motion recognition is essential for user-centric services such as surveillance-based security, elderly condition monitoring, exercise tracking, daily calories expend analysis, etc. It is typically based on the movement data analysis such as the acceleration and angular velocity of a target user. The existing motion recognition studies are only intended to measure the basic information (e.g., user’s stride, number of steps, speed) or to recognize single motion (e.g., sitting, running, walking). Thus, a new mechanism is required to identify the transition of single motions for assessing a user’s consecutive motion more accurately as well as recognizing the user’s body and surrounding situations arising from the motion. Thus, in this paper, we collect the human movement data through Android smartphones in real time for five targeting single motions and propose a mechanism to recognize a consecutive motion including transitions among various motions and an occurred situation, with the state transition model to check if a vulnerable (life-threatening) condition, especially for the elderly, has occurred or not. Through implementation and experiments, we demonstrate that the proposed mechanism recognizes a consecutive motion and a user’s situation accurately and quickly. As a result of the recognition experiment about mix sequence likened to daily motion, the proposed adoptive weighting method showed 4% (Holding time=15 sec), 88% (30 sec), 6.5% (60 sec) improvements compared to static method.
Abstract
The aim of this study was to perform a meta-analysis of the learning outcomes of immersive learning technologies in English language teaching (ELT). This study examined 12 articles, yielding a total of 20 effect sizes. The Comprehensive Meta-Analysis (CMA) program was employed for data analysis. The findings revealed that the overall effect size was 0.84, implying a large effect size. Additionally, the mean effect sizes of the dependent variables revealed a large effect size for both the cognitive and affective domains. Furthermore, the study analyzed the impact of moderator variables such as sample scale, technology type, tool type, work type, program type, duration (sessions), the degree of immersion, instructional technique, and augmented reality (AR) type. Among the moderators, the degree of immersion was found to be statistically significant. In conclusion, the study results suggested that immersive learning technologies had a positive impact on learning in ELT.
Abstract
Small and medium enterprises need a wide range of information regarding technologies, industries, market conditions, other companies, and products to facilitate identification of new growth opportunities and to respond quickly to changes in the business environment. Collecting such information, however, requires the input of resources (human resources, cost, and time), and the lack of marketability resulting from the shortage of such resources is often cited as the reason small and medium often fail in their effort to commercialize their technology. The purpose of this study was to analyze the environmental factors such as market concentration and the current level of competition to enable companies to make the necessary decisions when considering market entry following R&D planning and technology commercialization, or after the development of a new product. Also, an expert-based 5FORCE analysis can be performed using the KMAPS, an industry and market intelligence system: this facilitates automatic production of analyses based on the corporate finance and transaction data. The key contribution of the study is that it facilitates conveniently and quickly analyzing the 5 Forces, which had been a difficult task in the environmental analysis, and that the outcomes of this study empower companies in the decision-making process.
Abstract
Occupational therapists (OTs) interface patients much longer than other healthcare workers and, thus, are likely to experience a high level of stress because of work, physical fatigue, lack of professional knowledge and skills, and problematic relationships with patients, etc. This study examined the quality of the lives of OTs and the workplace which is an influential factor. This study recruited 200 OTs and used a professional quality of life scale. We found that, only 27.0% experience high levels of compassion satisfaction (CS) and 80.5% and 74.5% experience high or average levels of burnout (BO) and secondary traumatic stress (STS) of compassion fatigue (CF), respectively. We discovered that CS was higher in temporary employees; and CF was higher in females, subjects in their 30s; and those working at general hospitals. Also, BO was higher in those with one to five years of clinical experience and regular employees. Last, STS was higher in the group without stable income. This study verified that clinical experience is a critical factor that reduces BO, and excessive workload outside of treatment lowers the professional quality of life. This suggests that work environment and regulations related to OTs must be improved to increase CS and reduce CF.
Abstract
Narratives arouse diverse and rich affective responses to recipients, and this is one of the reasons why narratives are universal and popular. Computational studies on narratives have established a formal model or system of the affective response based on the theory in psychology or media research, and have analyzed or generated a narrative that can evoke a specific affective response. In this paper, we propose a new computational model that can detect the affective response expected to appear in the narrative based on the narrative agent’s knowledge. First, we designed a narrative representation model that can elaborately express the event structure and the agent’s knowledge as well. Additionally, an analysis method was proposed to detect the three affective responses and the related situational information. Then, we validated the model through a case study about an actual movie narrative. Through the case study, we confirmed that the model captures the affective responses of the audience. The proposed model can be effectively used for the narrative analysis and the creation that must consider the affective responses of the recipient.
Abstract
With the rapid growth of high-tech, the development of cultural and creative industries has gradually become the focus of national industrial development. With the proposal of China's “Belt and Road” strategy, the role of cultural and creative industries in the provinces and cities along the “Belt and Road” in the entire international trade is becoming increasingly critical. It is necessary to explore solutions to improve the competitiveness of China's cultural and creative industries, factoring the surrounding cities of the "Belt and Road" as an example. Thus, this paper proposes the six-element diamond model based on innovation capability and government support to render a comprehensive evaluation of the competitiveness of the cultural and creative industries in the 31 provinces and cities across the country. The results show that the overall competitiveness of the 18 provinces and cities along the “Belt and Road” cultural and creative industries is weak. Focusing on the 18 provinces and cities along the “Belt and Road”, using the linear regression measurement model quantitative analysis, the four types of influencing factors affecting the development of the competitiveness of cultural and creative industries along the “Belt and Road” were obtained. Finally, according to the four types of influence, the competitiveness improvement plan is proposed from the four aspects: government role, consumption preference, industrial innovation ability, and the introduction of high-quality talent.
Abstract
This research explored the impact the thoughts and opinions of lecturers teaching university general English courses have on their virtual classes and discussed the differences and similarities in their perspectives. As a research methodology, the Q-method was used, which usually consists of five steps: Q-population, Q-statements, Q-participants, Q-sorting, and factor analysis using PQMethod, a computer software program. A total of 30 statements were finally selected and 26 people directly involved in teaching online university general English courses were selected as participants in this study. Based on the results of the factor analysis, this research discovered three different factors, which were interaction & content-oriented, online teaching favored, and content-oriented. The results of this study can contribute to developing the future directions of virtual university general English classes to help learners improve their English skills and prepare lecturers for the future.
Abstract
Problem solving in real life is a core competency commonly adopted in several subjects recently. The real-world problems were rarely resolved with only one domain of knowledge. Thus, convergence education is becoming a trend in many countries. As on-line education is revitalized because of COVID-19, the need for changes to STEAM education is emerging. In December 2019, the on-line education systems quickly settled in schools because of COVID-19. During the first semester of 2020, most school education was conducted as virtual/distance learning classes. It is necessary to analyze how much technology has been used in the STEAM classes in Korea. This paper analyzes the research papers related to the STEAM education in Korea conducted in the last 10 years. By doing this, we analyze what kind of edutechs we were interested in, what kind of STEAM education actually occurred, and to whom it was conducted. The previous research papers analyzed in this paper are the papers in the Korea Citation Index accredited journals and candidate journals. Finally, this paper proposes educational changing factors to be considered for future technology-based STEAM education.