• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1046-1048
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• 2003

Recently, various researches in respect of the positioning technologies using satellites and the other sensors have made location-based services (LBS) more common and accurate. Consequently, concern about position information has been increasing. However, since these positioning systems only focus on user's position, it is difficult to know the user's attitude or detailed behaviors at the specific position. It is worthy to study on how to acquire such human attitude or behavior, because those information is useful to know the context of the user. In this paper, the sensor unit consisting of three dimensional accelerometer was attached to human body, and autonomously measured the perpendicular acceleration of ordinary human behaviors including activity modes such as walking, running, and transportation mode using transportation such as a train, a bus, and an elevator. Subsequently, using the classified measurement results, the method to identify the human activity modes was proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Asia pacific journal of information systems
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• v.10 no.4
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• pp.37-56
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• 2000

This study proposes the concept and measurement of object-oriented systems modelling activity based on the previous research in the area of systems engineering, object-oriented modelling, and information systems. The modelling activity is related to information systems development success for exploring the correlation of each other. The object-oriented modeling activity is found to be related to user satisfaction with developed information systems, But the modeling activity does not have relation to the other successes, such as cost, development schedule, and maintenance. This study contributes to systems development modeling research, systems success, and object-oriented systems modelling research. Practically, the results support the usefulness of object-oriented modelling effort in the field in terms of user satisfaction.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.57-64
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• 2015

Activity recognition technology is gaining attention because it can provide useful information follow user's situation. In research of activity recognition before smartphone's dissemination, we had to infer user's activity by using independent sensor. But now, with development of IT industry, we can infer user's activity by using inner sensor of smartphone. So, more animated research of activity recognition is being implemented now. By applying activity recognition system, we can develop service like recommending application according to user's preference or providing information of route. Some previous activity recognition systems have a defect using up too much energy, because they use GPS sensor. On the other hand, activity recognition system which Google released recently (Google Activity Recognition) needs only a few power because it use 'Network Provider' instead of GPS. Thus it is suitable to smartphone application system. But through a result from testing performance of Google Activity Recognition, we found that is difficult to getting user's exact activity because of unnecessary activity element and some wrong recognition. So, in this paper, we describe problems of Google Activity Recognition and propose AGAR(Advanced Google Activity Recognition) applied method to improve accuracy level because we need more exact activity recognition for new service based on activity recognition. Also to appraise value of AGAR, we compare performance of other activity recognition systems and ours and explain an applied possibility of AGAR by developing exemplary program.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2411-2420
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• 2000

Far detecting variaus camputer intrusians effectively, many researches have develaped the misuse based intrusian detectian systems. Recently, warks related ta anamaly detectian, which have impraved the drawback .of misuse detectian technique, have been under focus. In this paper, a new clustering algarithm based an support constraint far generating user's narmal activity patterns in the anamaly detectian can praposed. It can grant a user's activity .observed recently ta mare weight than that .observed in the past. In order that a user's anamaly can be analyzed in variaus angles, a user's activity is classified by many measures, and far each .of them user's narmal patterns can be generated. by using the proposed algarithm. As a result, using generated narmal patterns, user's anamaly can be detected easily and effectively.

• Journal of Korean Society for Quality Management
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• v.44 no.1
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• pp.199-214
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• 2016

Purpose: The present study was designed to examine the casual relationships among performance and arts information service quality, initial trust, user satisfaction, reuse intention and prosumer activity in social network service(SNS). Also, we intended to explore significant factors on use performance of SNS through causal model analysis in the viewpoint of total effect. Methods: As a survey tool, questionnaire has obtained validity and reliability through literature survey, exploratory survey and pretest and sample 403 was selected. For statistical treatment of pretest and main analysis, SPSS18.0 and AMOS18.0 were employed and structural equation model was employed as analysis method. Results: Result of this study shows as follows. Two factors (precision and reciprocal action) have an effect on user satisfaction, initial trust, reuse intention and prosumer activity. We found that with an importance of initial trust, prosumer activity can be a useful and significant factor in causal relationship of SNS. Conclusion: The present study shows that two factors(precision and reciprocal action) in via of initial trust, were important factors that related companies have to emphasize to raise performance, And also we confirmed new factor 'prosumer activity' through this study. However, the present study has some limitations to be studied in the future.

• Knowledge Management Research
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• v.16 no.3
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• pp.81-102
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• 2015

With the rapid development of digital media, there has been a huge change in a way of communication, a process of information diffusion and a role of traditional media. Not like mass media, social media enables users to generate and tap into the opinions of a larger world. From that reason, social media is impacting marketing strategies. However, still social media marketing researches just focus on case study, analysis of users motivation or analysis of power user's usage pattern. Word-of-mouth has always been important especially in marketing area. In social media, word-of-mouth depends on each user that's why this research focuses on individual user's activity in SNS. I defined 4 factors (produce, diffusion, network size, activity of network size enlarge) that are effect on activity and verified hypothesis by multiple regression analysis, hierarchical regression analysis and moderated multiple regression.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1570-1594
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• 2017

Social network services have recently changed from environments for simply building connections among users to open platforms for generating and sharing various forms of information. Existing user reputation computation methods are inadequate for determining the trust in users on social media where explicit ratings are rare, because they determine the trust in users based on user profile, explicit relations, and explicit ratings. To solve this limitation of previous research, we propose a user reputation computation method suitable for the social media environment by incorporating implicit as well as explicit ratings. Reliable user reputation is estimated by identifying malicious information raters, modifying explicit ratings, and applying them to user reputation scores. The proposed method incorporates implicit ratings into user reputation estimation by differentiating positive and negative implicit ratings. Moreover, the method generates user reputation scores for individual categories to determine a given user's expertise, and incorporates the number of users who participated in rating to determine a given user's influence. This allows reputation scores to be generated also for users who have received no explicit ratings, and, thereby, is more suitable for social media. In addition, based on the user reputation scores, malicious information providers can be identified.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.2
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• pp.61-68
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• 2010

As a result of the growth of mobile devices such as PDA and cellular phone, a user can utilize various digital contents everywhere and anytime. However, mobile devices have the limited resources and interaction mechanisms. This paper introduces the schema for a user activity estimation and its application in order to overcome the poor user interface and limited resource problems. We are able to supplement lacking the user interface of mobile devices by using the user activity estimation proposed in this paper, and its application is a intelligent control interface for the display power on or off which can effectively utility the battery of the mobile device.