• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.115-119
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• 2024

With the increase in the number of ICT industry workers, there is a demand for research on preventing VDT syndrome. However, existing posture correction products mostly rely heavily on cameras or sensors in wearable devices. In this paper, we have developed a posture correction system that utilizes built-in cameras and circular pressure sensors to collect posture information. Additionally, the system provides a personalized service by capturing the correct posture of the user initially and monitoring the user's posture based on that input. By precisely correcting postures during users' daily tasks, this system aims to prevent and improve VDT syndrome, ultimately enhancing the efficiency of ICT industry workers.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.70-76
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• 2010

In this paper, we propose a personalized Chinese character education system using RankBoost algorithm on a smartphone. In a typical Chinese character education scenario, a trainee is supplied with a finite number of Chinese characters as an input set in the beginning. And, as the training session repeats, the trainee will notice her/his difficult characters in the set which she/he hardly answers. Those characters reflect their personalized degrees of difficulty. Our proposed system constructs these personalized degrees of difficulty using RankBoost algorithm. In the beginning, the algorithm start with the set of Chinese characters, of which each is associated with the same weight values. As the training sessions are repeated, the algorithm increase the weights of Chinese characters that the trainee mistakes, thereby eventually constructs the personalized difficulty degrees of Chinese characters. The proposed algorithm maximizes the educational effects by having the trainee exposed to difficult characters more than easy ones.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1015-1025
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• 2017

Bayesian personalized ranking (BPR) is a state-of-the-art recommendation system techniques for implicit feedback data. Unfortunately, there might be a loss of information because the BPR model considers only the binary transformation of implicit feedback that is non-binary data in most cases. We propose a modified BPR method using a level of confidence based on the size or strength of implicit feedback to overcome this limitation. The proposed method is useful because it still has a structure of interpretable models for underlying personalized ranking i.e., personal pairwise preferences as in the BPR and that it is capable to reflect a numerical size or the strength of implicit feedback. We propose a computation algorithm based on stochastic gradient descent for the numerical implementation of our proposal. Furthermore, we also show the usefulness of our proposed method compared to ordinary BPR via an analysis of steam video games data.

• Physical Therapy Rehabilitation Science
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• v.10 no.4
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• pp.421-429
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• 2021

Objective: The purpose of this study was to examine the effect of personalized complex aerobic training programs using wearable device on cardiovascular and respiratory functions in community based female elderly. Design: One group pre-post intervention study. Methods: Twenty-one older female participants lived in 'D' city were included. The personalized complex aerobic training program using wearable devices was applied to all participants for 4 weeks, 3 times a week, 30 minutes for per session. The participants' blood pressure, heart rate, oxygen saturation, respiration rate, submaximal exercise stress test, pulmonary function test and respiratory muscle strength test were evaluated before and after the complex training program. Results: After intervention, resting diastolic blood pressure, resting systolic blood pressure and the systolic blood pressure after submaximal exercise stress test were significantly decreased over time (p<0.05), and the submaximal exercise stress test duration were significantly increased over time (p<0.05). The maximal inspiratory pressure (MIP) was significantly increased compare to before the intervention (p<0.05). Conclusions: This study showed that personalized complex training program using wearable device can provide personalized exercise intensity according to cardiopulmonary function that give feedback, and these interventions have a significant effect on improving the cardiovascular and respiratory system functions of the female elderly in the community dwelling.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.19-28
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• 2021

Nowadays, interest in health care is increasing due to Coronavirus (COVID-19), and a lot of people are doing home training as there are more difficulties in using fitness centers and public facilities that are used together. In this paper, we propose a personalized exercise recommendation algorithm using personalized propensity information to provide more accurate and meaningful exercise recommendation to home training users. Thus, we classify the data according to the criteria for obesity with a k-nearest neighbor algorithm using personal information that can represent individuals, such as eating habits information and physical conditions. Furthermore, we differentiate the exercise dataset by the level of exercise activities. Based on the neighborhood information of each dataset, we provide personalized exercise recommendations to users through a dimensionality reduction algorithm (SVD) among model-based collaborative filtering methods. Therefore, we can solve the problem of data sparsity and scalability of memory-based collaborative filtering recommendation techniques and we verify the accuracy and performance of the proposed algorithms.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.1
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• pp.49-58
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• 2011

Recently, GIS(Geographic Information System) has been developed to personalized service for providing the specialized services that is aimed to personal user based on mobile communication. The existing GIS system provides comprehensive and simple information but GIS System for personalized service must provide the adjustive information through the personal interest profile based on POI(PoInt of Interest). This paper describes the intelligent retrieval geographical information service module for providing personal oriented geographic information service. Our proposal model consists of user preference profile, acquisition of POI through hybrid network (Wireless LAN, CDMA), service platform and implementation of prototype system. Implementation model can apply to the life information service like restaurant, oil station, convenient store and etc.

• The Journal of Society for e-Business Studies
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• v.15 no.2
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• pp.1-18
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• 2010

Mobile Communication Companies are trying to increase data services rather than telephone communication services that already became saturated as the competition of mobile communication market gets intensified. However, it is hard and time-consuming for customers to find desired mobile phone wireless services because of the limitation of screen and speed of the mobile phone. Therefore, the market does not grow rapidly as mobile communication companies expected. In our research, we suggest a personalized wireless service recommendation system that considers each individual context by using geographic information and wireless internet usage logs to overcome the mentioned problems. In order to design and implement the system, we conducted clustering analysis on base stations and real service usage logs of each base station, and suggested a personalized recommendation system based on collaborative filtering that uses the clustering results. Moreover, we verified the performances of our system with experiments.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.147-153
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• 2014

To create a personal menu, there are a number of considerations. Personal menus are different depending on the dietary therapy for disease, diet for weight control. In addition, the menu you choose, depending on the personal preference and the season, the weather, current personal feelings may differ. An individual should expect to recommend a balanced diet, taking nutritional status just for health care. In this paper, we propose a personalized menu recommendations System framework to meet such needs. To recommend menus the system receives data of the body's individual circumstances, ingredients situation, environmental conditions, psychological condition, emotional condition and provides a recommended menu by performing the inference using the ontology generated from external application systems. In order to provide such services, Internet of Things (IoT) environment should be the foundation. In this paper, we propose a personalized diet recommendation system framework in the IoT standardization environment that has oneM2M common service platform.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.1-9
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• 2010

Diabetes is a type of metabolic disease presented by high blood sugar and that leads to significantly decrease the quality of life causing various symptoms. It is essential to manage a systematic menu for preventing such diabetes even though there are some ways for it including diet, physical exercise, medicinal prescription, and so on. This study proposes a smart phone based personalized menu management system for achieving the systematic diabetes management. At the present time almost menu systems for diabetes patients are subjectively prescribed by dietitians or doctors and that does not reflect current situations and personal preferences. The system proposed in this study provides the menu for diabetes patients according to season, weather, time, and personal preferences. In particular, the recipe and personalized menu for patients can be provided without limiting any time and location based on smart phone services, and its menu can easily be changed or selected by the phone.