• The Journal of Bigdata
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• v.6 no.2
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• pp.51-60
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• 2021

The market size of wearable devices is growing rapidly every year, and manufacturers around the world are introducing products that utilize their unique characteristics to keep up with the demand. Among them, smart watches are wearable devices with a very high share in sales, and they provide a variety of services to users by using information collected in real-time. The quality of service depends on the accuracy of the data collected by the smart watch, but data measurement may not be possible depending on the situation. This paper introduces a method to restore data that a smart watch could not collect. It deals with the similarity calculation method of trajectory information measured over time for data restoration and introduces a procedure for restoring missing sections according to the similarity. To prove the performance of the proposed methodology, a comparative experiment with a machine learning algorithm was conducted. Finally, the expected effects of this study and future research directions are discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.84-86
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• 2022

Task-oriented chatbots prevail in various filed with the artificial intelligent dialogue system. The need for chatbots in customer services is growing, especially in education businesses given that there are many user inquiries and consultation requests. However, current dialogue systems only function as simple reactions or predetermined and frequently used actions. Meanwhile, the research about customized recommendation systems through artificial intelligence is very active with a wide variety of educational content. Although a dialogue system and a recommendation system is a core element in this domain, it has a limitation in that it is being conducted separately. Therefore, we present a study on a recommendation system that can recommend user-customized lectures combined with a dialogue system. With this combination, our system can respond to additional functions beyond these limitations. Through our research, we expect that work efficiency and user satisfaction will be improved by applying chatbots in education domains that are becoming more diversified and personalized.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.37-43
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• 2023

In today's business landscape, collaboration and interoperability are crucial for organizational success and profitability. However, integrating operations across multiple organizations is challenging due to differing roles and policies in Identity and Access Management (IAM). User-centric identity (UCI) adopts a personalized approach to digital identity management, centering on the end-user for authentication and access control. It provides a decentralized system that ensures secure and customized access for each user. UCI aims to address complex security challenges by aligning access privileges with individual user requirements. This research delves into UCI's ability to streamline resource access amidst conflicting IAM roles and protocols across various organizations. The study presents a UCI-based multi-domain access control (MDAC) framework, which encompasses an ontology, a unified method for articulating access roles and policies across domains, and software services melding with UCI infrastructure. The goal is to enhance organizational resource management and decision-making by offering clear guidelines on access roles and policy management across diverse domains, ultimately boosting companies' return on investment.

• PNF and Movement
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• v.22 no.1
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• pp.101-111
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• 2024

Purpose: The purpose of this study was to investigate physical activity as a risk factor for neck pain recurrence using the National Health Insurance Data Sharing Service that utilizes a nationwide cohort in South Korea. Methods: Medical records spanning a two-year period were extracted from the National Health Insurance database for 541,937 patients who sought healthcare services for neck pain (ICD 10 codes: M54.2) in 2020 and completed the national health examination survey. Selected variables for analysis included age, gender, health insurance premium decile, regional health vulnerability index, body mass index (BMI), acuity, blood pressure, and types of physical activity. A mixed-effect multivariate logistic regression analysis was conducted to examine the recurrence rate of neck pain and identify risk factors for neck pain recurrence. Results: Among the participants, 124,433 patients (23.0%) experienced a recurrence of neck pain within two years, with higher recurrence rates observed among older individuals and females. Regression analysis revealed that the risk of neck pain recurrence increased with age (OR=1.51), being female (OR= 1.10), being a medical aid recipient (OR=1.51), and having anaerobic (OR=1.04) or vigorous physical activities (OR=1.06). By contrast, an increased health insurance premium decile (OR=0.96) and having moderate physical activity (OR=0.97) were associated with a decreased risk of neck pain recurrence. Conclusion: This study highlights the importance of moderate physical activity as an effective strategy for reducing the recurrence of nonspecific neck pain, underscoring the necessity for personalized physical activity programs for patients.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.141-147
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• 2024

Recently, there has been growing interest in personalized services tailored to an individual's preferences. This has led to ongoing research aimed at recognizing and leveraging an individual's personality traits. Among various methods for personality assessment, the OCEAN model stands out as a prominent approach. In utilizing OCEAN for personality recognition, a multi modal artificial intelligence model that incorporates linguistic, paralinguistic, and non-linguistic information is often employed. This paper examines the impact of the margin value set for extracting facial areas from video data on the accuracy of a personality recognition model that uses facial expressions to determine OCEAN traits. The study employed personality recognition models based on 2D Patch Partition, R2plus1D, 3D Patch Partition, and Video Swin Transformer technologies. It was observed that setting the facial area extraction margin to 60 resulted in the highest 1-MAE performance, scoring at 0.9118. These findings indicate the importance of selecting an optimal margin value to maximize the efficiency of personality recognition models.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.159-165
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• 2024

This study investigates the comprehensive system for collecting intelligent learning activity data tailored to learner-centered personalized education. We compared and analyzed the characteristics of xAPI, Caliper analytics, and cmi5, which are learning activity data collection standards, and established a system that allows not only standardized data but also non-standardized learning activity data to be stored as big data for artificial intelligence learning analysis. As a result, the system was structured into five stages: defining data types, standardizing learning data using xAPI, storing big data, conducting learning analysis (statistical and AI-based), and providing learner-tailored services. The aim was to establish a foundation for analyzing learning data using artificial intelligence technology. In future research, we will divide the entire system into three stages, implement and execute it, and correct and supplement any shortcomings in the design.

• Information Systems Review
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• v.23 no.4
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• pp.69-86
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• 2021

As artificial intelligence technology advances, personalized recommendation systems using big data have attracted huge attention. In the case of beauty products, product preferences are clearly divided depending on customers' skin types and sensitivity along with individual tastes, so it is necessary to provide customized recommendation services based on accumulated customer data. Therefore, by employing deep learning methods, this study proposes a neural network-based recommendation model utilizing both product search history and context information such as gender, skin types and skin worries of customers. The results show that our model with context information outperforms collaborative filtering-based recommender system models using customer search history.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.131-141
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• 2024

The aging population and worsening lifestyle habits have increased the risk of chronic diseases. This has heightened the importance of preventive healthcare, particularly through personalized health management services based on individual health data. Despite this, the domestic digital healthcare industry remains underdeveloped. Given the need for acceptance from both consumers and providers, this study uses the Analytic Hierarchy Process (AHP) to identify success factors for health management service platforms. AHP evaluates the relative importance of various factors to aid decision-making. Results show that providers prioritize data analysis and platform design, laws and regulations, and data standardization, while consumers prioritize system stability, laws and regulations, and system security. These findings highlight the need for strategies to bridge the expectation gap to effectively promote health management service platforms.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.23-30
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• 2024

In this paper, we propose a deep learning-based personalized senior care service application. The proposed application uses Speech to Text technology to convert the user's speech into text and uses it as input to Autogen, an interactive multi-agent large-scale language model developed by Microsoft, for user convenience. Autogen uses data from previous conversations between the senior and ChatBot to understand the other user's intent and respond to the response, and then uses a back-end agent to create a wish list, a shared calendar, and a greeting message with the other user's voice through a deep learning model for voice cloning. Additionally, the application can perform home IoT services with SKT's AI speaker (NUGU). The proposed application is expected to contribute to future AI-based senior care technology.

• Industry Promotion Research
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• v.5 no.3
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• pp.73-79
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• 2020

This study aims to study the problems and solutions of institutional support for the elderly living alone, focusing on the General Support for Living Alone Elderly announced by the Ministry of Health and Welfare in 2018. Results, First, a customized support system for the elderly living alone should be introduced. In order to improve the life satisfaction of the elderly living alone, it is necessary to develop a program that meets the most basic daily life needs, and a specific plan and a support system to link services should be prepared. Second, it is necessary to increase social interest in the elderly living alone. Solving problems for the elderly living alone should be preceded by social interest in the elderly living alone. For this, it is necessary to strengthen the social network. Third, it proposes legislation and amendment for the elderly living alone. Some revisions of existing laws have limitations, and are resolved through individual laws, such as standards and definitions for various types of elderly jobs, reorganization of the delivery system including agencies dedicated to elderly jobs, workers-related regulations, and preferential purchase systems for senior products. It is desirable to do. In conclusion, welfare support for the elderly living alone should be comprehensive and comprehensive. For the welfare of the elderly living alone, personalized care services should be provided first, and social support for the elderly living alone should be promoted on the basis of increasing social interest, and laws and revisions must be actively and proactively made for the elderly living alone.