• Journal of KIISE
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• v.43 no.7
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• pp.781-785
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• 2016

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.171-183
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• 2007

Urban transport demand is derived from activity participation. A variety of individual daily activities based on the decisions on activity participation result in collective spatial behavior. The travel derived from the effort to overcome the spatially distributed locations of adjacent activities represents the detailed structural relationships among activities. An activity-based approach provides an important framework of analyzing contemporary urban daily life in the sense that it studies the interaction between individuals' daily decision making and social practice in time and space, on the one hand, and socio-spatial environment on the other. The current study identifies representative patterns of urban daily activity implementations and analyzes the correlation between representative patterns and individuals' characteristics and contextual characteristics. The study shows that urban daily activity patterns can be grouped in a limited number of representative patterns, which are systematically correlated with socio-spatial characteristics. The results provide related transportation policy implications.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.2
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• pp.91-98
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• 2017

Background & Object: Basic daily activity screening tool such as the Modified Barthel Index (MBI) has been used commonly in rehabilitation clinic and community based rehabilitation setting. Previous studies have shown the significant relations between the level of daily activities and driving ability on stroke or elderly people. However, there is a lack of studies to investigate the usefulness of MBI on prediction of driving ability for stroke patient. This study was to predict driving abilities of stroke survivor using Korean version Modified Barthel Index (K-MBI). Methods: A sample of 48 patients with stroke in rehabilitation hospital was recruited. All participants were tested level of basic daily activities using K-MBI. The driving ability of participants was tested using virtual reality driving simulator. The predictive validity was calculated of the K-MBI among pass or fail group of driving simulator test using receiver operating characteristics curves. Results: The cut-off score of >86.5 on the K-MBI is proper sensitivity to predict on driving performance ability. Conclusion: This pilot result offers clinical reference to therapists and caregivers for reasoning on driving recommendation period during rehabilitation stage of stroke survivors. Further studies need to identify prediction using real on-road test in a large population group.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.487-490
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• 2007

정보통신 서비스의 발달에 의해, 미래 사회의 사람들의 일상생활과 사회활동의 모습을 그린 미래상을 분석하고, 정보통신서비스의 상용화를 전망해서 미래 정보통신 시스템 구성법 및 어플리케이션을 명확하게 하고, 이를 기반으로 중장기적인 미래 핵심 및 세부요소 기술을 도출하고자 한다. 따라서 본 논문은 사용자의 다양한 요구를 토대로 비용 대비 효과와 효율성이 높은 미래 기술을 예측하여, 기술개발정책 수립을 지원하고 기술개발의 경쟁력 확보에 기여할 것이다. 또한, 산업계, 학회 등의 연구개발의 촉진, 표준화 활동에 초석을 마련하는 것을 목적으로 한다.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.480-488
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• 2010

The aim of this study were to examine the effects of virtual reality program on balance and activities of daily living in children with spastic cerebral palsy (CP) using the Pediatric Balance Scale (PBS) and the Functional Independence Measure for Children (WeeFIM), and to measure relationship between the PBS and the WeeFIM. For this, A total of 20 spastic CP classified as the Gross Motor Function Classification System (GMFCS) I and II were employed. The Participant's were allocated randomly to 2 groups: a virtual reality group (n=10) and the control group (n=10). Both groups received muscle strengthening exercise for 3 sessions, 30 minutes per week over a 12 week period. The virtual reality group practiced additional virtual reality program. The virtual reality group showed significant increases in balance (p<0.05) and activities of daily living (p<0.05). There were a significant correlation between the PBS and the WeeFIM (p<0.05). Application of the virtual reality program to treat the spastic CP will be feasible and suitable. And the PBS was a useful tool to predict activities of daily living in the spastic CP.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.544-553
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• 2014

Activity-based models analyze individuals' various daily activities that are identified as a decision-making unit for transportation planning. In other words, it is the model that determines the types of activities according to the social, economic and situational characteristics of the groups with the same activity patterns and predicts individuals' activity time, distance, spatial movement and transportation mode. The activity-based model is a method of estimating more efficient and realistic demand in transportation forecasting because traffic is regarded as a complex decision-making process that an individual and other people participate in. In this paper, we grasp the factors affecting choice behavior of activity pattern and analyze choice behavior of activity pattern based on multi-dimensional characteristic of each person. First, we classify activity types of reviewing the trip chain and activity purpose. Next, we identified preferable activity types using complicated characteristics of main agent of activity. We concluded that choice behavior of activity pattern is dependent on complex characteristics of each agent, and further multi-dimensional characteristics of each person are affected over the whole decision process of activity schedule.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.419-430
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• 2014

An accurate prediction of electricity supply and demand is important for daily life, industrial activities, and national management. In this paper electricity sales is predicted by time series modelling. Real data analysis shows the transfer function model with cooling and heating days as an input time series and a pulse function as an intervention variable outperforms other time series models for the root mean square error and the mean absolute percentage error.

• Journal of IKEEE
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• v.14 no.1
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• pp.17-24
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• 2010

This paper presents calorie expenditure prediction model of daily activity of elderly living alone for LBS(Location Based Service) applications. The proposed method is to describe the daily activity patterns of older adult using PIR (Passive InfraRed) motion sensors and to examine the relationships between physical activity and calorie expenditure. The developed motion detecting system is composed of a sensing system and a server system. The motion detecting system is a set of wireless sensor nodes which has PIR sensor to detect a motion of elder. Each sensing node sends its detection signal to a home gateway via wireless link. The home gateway stores the received signals into a remote database. The server system is composed of a database server and a web server, which provides web-based monitoring system to caregivers for more effective services. The experiment results show the adaptability and feasibility of the calorie expenditure model.

• Korean Journal of Culture and Social Issue
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• v.28 no.4
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• pp.661-690
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• 2022

Adolescent delinquency is a substantial social problem that occurs in both offline and online domains. The current study utilized random forest algorithms to identify predictors of adolescents' online and offline delinquency. Further, we explored the applicability of classic delinquency theories (social learning, strain, social control, routine activities, and labeling theory). We used the first-grade and fourth-grade elementary school panels as well as the first-grade middle school panel (N=4,137) among the sixth wave of the nationally-representative Korean Children and Youth Panel Survey 2010 for analysis. Random forest algorithms were used instead of the conventional regression analysis to improve the predictive performance of the model and possibly consider many predictors in the model. Random forest algorithm results showed that classic delinquency theories designed to explain offline delinquency were also applicable to online delinquency. Specifically, salient predictors of online delinquency were closely related to individual factors(routine activities and labeling theory). Social factors(social control and social learning theory) were particularly important for understanding offline delinquency. General strain theory was the commonly important theoretical framework that predicted both offline and online delinquency. Findings may provide evidence for more tailored prevention and intervention strategies against offline and online adolescent delinquency.

• The Journal of Korean society of community based occupational therapy
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• v.9 no.1
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• pp.35-45
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• 2019

Objective : The purpose of this study was to investigate the factors influencing stroke in community-dwelling adults. Methods : This study used raw data from the community health survey in 2016. Among the 228,452 subjects who participated in the survey, 225,003 (98.5%) of them were included in the analysis of this study. The sociodemographic characteristics were selected as gender, age, region, income, marital status, and comorbidity was selected as osteoporosis, hypertension, diabetes, dyslipidemia, myocardial infarction and arthritis. Health-related quality of life was assessed by EuroQol-5 Dimension(EQ-5D) and the subcategory of EQ-5D (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) were included in the analysis. Dependent variables were stroke, and the independent variables were the 16 variables described above. Statistical analysis was performed using binomial logistic regression analysis. Results : In sociodemographic variables, stroke was predicted by men, aging, and lower income levels. In comorbidity variables, stroke was predicted by hypertension, diabetes mellitus, hyperlipidemia, and myocardial infarction. In sub-domains of health-related quality of life, stroke was predicted by self-care, usual activities, mobility, anxiety/depression, and pain/discomfort. Conclusion : These finding suggest that it is needed development of a customized health promotion program for the improvement of self-care and activities of daily living in community-dwelling stroke survivors.