• 디지털융복합연구
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• 제15권9호
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• pp.79-85
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• 2017

본 연구에서는 최근 증가하는 보행 중 스마트폰의 이용실태를 파악하고 스마트폰 이용행태가 보행안전도의 인적요인에 어떠한 영향을 미치는지 살펴보았다. 보행자의 스마트폰 사용동기 및 실태를 파악하기 위해 스마트폰을 사용하는 20~30대를 대상으로 온라인 설문조사를 진행하였으며 그 결과 보행 중 스마트폰 사용경험은 98.6%로 대부분의 응답자가 보행 중 스마트폰 사용경험이 있는 것으로 나타났다. 선행연구에서 17개의 이용 동기 요소를 추출하여 설문을 실시한 후 요인분석을 통해 2개의 요인으로 추출하였다. 다중회귀분석 결과 F-value는 12.967, P-value는 0.000으로 유의미한 것으로 분석되어 스마트폰 이용행태는 보행안전도의 인적요인에 영향을 미치는 것으로 나타났다. 특히 스마트폰 이용 동기가 유행성에 있는 응답자들은 보행안전도 인적요인 중 스마트폰사용으로 인한 사고위험에 영향을 미치는 것으로 분석되었다.

• International Journal of Advanced Culture Technology
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• 제5권2호
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• pp.98-105
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• 2017

Smartphones are fast growing in the IT market and are the most influential devices in our daily life. Smartphones are being studied for their use in body sensor networks with excellent processing power and wireless communication technology. In this paper, we propose a coordinator design that provides data collection, classification, and display using based on Android-smartphone in multiple sensor nodes. The coordinator collects data of sensor nodes that measure biological patterns using wireless communication technologies such as Bluetooth and NFC. The coordinator constructs a network using a multiple-level scheduling algorithm for efficient data collection at multiple sensor nodes. Also, to support different protocols between heterogeneous sensors, a data sheet recording wireless communication protocol information is used. The designed coordinator used Arduino to test the performance of multiple sensor node environments.

• 한국컴퓨터정보학회논문지
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• 제23권3호
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• pp.101-106
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• 2018

The purpose of this study is to analyze the patterns of unconsciousness smartphone use by using an app and a self-administered survey on smartphone addiction comparatively and examine differences between recognition and behavior about actual smartphone use and examine how smartphone addiction influences learning. With an app installed in smartphones, this author collected and analyzed data about users' unconsciousness using patterns for a month. According to the results, there were significant differences found in users' recognition and actual time for use and also frequency of turning on the display. Also, 22% of the subjects used their smartphone over 8 hours a day, and 76% more than 5 hours. Over 95% turned on the display more than 100 times a day, and in extreme cases, they did more than 300 times. In the meantime, users not only in the smartphone addiction high risk group and the potential risk group but also in the general user group are found to use their smartphone too long and too much and frequently turn on the display. The apps that the general user group is mainly using are entertaining apps, and their school records are rather good, so excessive use does not always lead to addiction or learning disorder. Therefore, if we develop more diverse contents for learning and provide digital literacy education, smartphone use will bring more positive effects instead. In follow-up research, the app should be corrected to collect more accurate information, and as variables in personal areas, this researcher will also measure depression, anxiety, stress, self-esteem, and emotional control, and so on to see how they are associated with smartphone use.

• 디지털산업정보학회논문지
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• 제11권2호
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• pp.113-120
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• 2015

Under the penetration of smartphones equipped with a variety of features grows globally, the efficient using of a variety of functions of smartphones has been increased. In accordance with this trend, a lot of researches on the remote control method using the smart phone for consumer products in home networks. Input methods of the current smpartphoes are typically button-based inputs through touching. The button input methods are inconvenient for people who are not familiar touch. Therefore, the researches on the different input schemes to replace the touch methods are required. In this paper, we propose a gesture based input method to replace the touch-sensitive input that of the existing smartphone applications, and a way to apply it to home networks. The proposed method uses three-axis acceleration sensor which is built into smatphones, and it also defines six kinds of gestures patterns that may be applied to home network systems by measuring the recognition rates.

• 실천공학교육논문지
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• 제16권4호
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• pp.577-585
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• 2024

This study analyzed the relationship between GPS-based physical activity patterns and mental health using Kaggle Student Life data. Data were collected over a 10-week period from 48 students at Dartmouth College through Android smartphones and included GPS, dark, and phone lock data, and measures such as the Patient Health Questionnaire-9 (PHQ-9), Loneliness Scale, the Positive and Negative Affect Schedule (PANAS), and Perceived Stress Scale. Using latitude and longitude data obtained from GPS measurements, various physical activity indicators were calculated, including the total distance traveled, average distance traveled, average distance traveled in the morning, average distance traveled in the afternoon, average distance traveled in the evening, and average distance traveled in the middle of the night. Pearson's correlation analysis was performed to explore the relationship between GPS-based physical activity patterns and mental health. The study results indicated a significant negative correlation between the average distance traveled in the afternoon and PHQ-9 scores. Results indicated that the higher the afternoon activity, the lower the depressive symptoms. There was a positive correlation be-tween the PANAS-Pos score and the average distance traveled in the evening, indicating that positive emotions tended to increase as evening activities increased. This finding suggests a relationship between physical activity at specific times and mental health.

• Journal of Korea Artificial Intelligence Association
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• 제2권1호
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• pp.1-6
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• 2024

In this paper, we develop an AI-based recommendation system that matches the specifications of smartphones from company 'S'. The system aims to simplify the complex decision-making process of consumers and guide them to choose the smartphone that best suits their daily needs. The recommendation system analyzes five specifications of smartphones (price, battery capacity, weight, camera quality, capacity) to help users make informed decisions without searching for extensive information. This approach not only saves time but also improves user satisfaction by ensuring that the selected smartphone closely matches the user's lifestyle and needs. The system utilizes unsupervised learning, i.e. clustering (K-MEANS, DBSCAN, Hierarchical Clustering), and provides personalized recommendations by evaluating them with silhouette scores, ensuring accurate and reliable grouping of similar smartphone models. By leveraging advanced data analysis techniques, the system can identify subtle patterns and preferences that might not be immediately apparent to consumers, enhancing the overall user experience. The ultimate goal of this AI recommendation system is to simplify the smartphone selection process, making it more accessible and user-friendly for all consumers. This paper discusses the data collection, preprocessing, development, implementation, and potential impact of the system using Pandas, crawling, scikit-learn, etc., and highlights the benefits of helping consumers explore the various options available and confidently choose the smartphone that best suits their daily lives.

• 한국안전학회지
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• 제32권3호
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• pp.74-82
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• 2017

This study was conducted to test the impacts of distractions caused by smartphones on pedestrians whilst walking alongside or across vehicular traffic in a high-density urban zone in South Korea. Through this study, we propose objective evidence for a link between the risk of traffic accidents and distractions from smartphones for pedestrians because of less likely notice activities surrounding road along their walking. This means that smartphones usage may cause inattentional blindness even during a simple activity that should require few cognitive resources. We conducted an experiment comparing pedestrian behavioral patterns of walking with smartphone distractions (such as listening to music with earphones or sending text messages) and normal walking without any distractions. In the experiment, participants walked along a pedestrian path prescribed by researchers and were observed at 8 points which were as follows: two observation points through which participants were instructed to listen to music whilst walking, two points where participants were instructed to send text messages, and four points through which participants were instructed not to use a smartphone at all. According to pedestrian behavior analysis, there is a trend for attention to be distributed amongst whatever other activities pedestrians are doing whilst walking. Therefore, this study proposes that pedestrians walking with such distractions are at a higher risk of traffic accidents compared to those who walk without such distractions. Thus, we advise for the South Korean government to consider ways to traffic policy that will enhance traffic safety for pedestrians.

• 인터넷정보학회논문지
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• 제16권3호
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• pp.95-104
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• 2015

최근 스마트폰 사용자들이 급격히 증가함에 따라 다양한 사회적 문제점들이 발생되고 있다. 대표적으로, 과도한 스마트폰의 사용과 잘못된 이용습관은 '스마트폰 중독(smartphone addiction)'과 '스마트폰 질병(smartphone disease)'과 같은 새로운 사회적 문제점들을 야기시키고 있다. 본 연구에서는 이러한 문제들을 사전에 예방하기 위해 스마트폰 사용자의 패턴을 방향센서를 이용해서 수집하고 분석해서 스마트폰 중독 및 질병을 예방하는 시스템을 제안한다. 제안된 시스템은 사용자의 스마트폰 사용형태와 사용시간을 실시간으로 분석하여 위험수준에 도달하였을 경우, 사용자에게 이를 알려 줌으로써 사용자 자신이 사전에 위험을 인지할 수 있도록 하며, 자신의 의지로 스마트폰 사용을 절제하도록 하여 스마트폰 중독 및 질병을 사전에 예방할 수 있도록 제안한다.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제32권4호
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• pp.154-160
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• 2021

Objectives: This study identified the association between excessive exposure to screen media and behavioral and emotional problems in elementary school students. Methods: A total of 331 parents of children aged 7-10 years were recruited from "The Kids Cohort for Understanding of Internet Addiction Risk Factors in Early Childhood (K-CURE)" study. Children's demographics, household media ownership, screen time, and behavioral/emotional problems were assessed using a parental questionnaire. Children's behavior/emotional problems were measured using the Korean version the of Child Behavior Checklist (K-CBCL) score. Results: The total K-CBCL score in the screen overuse group was 51.18±9.55, significantly higher than 47.28±10.09 in the control group (t=2.14, p=0.05). For each subscale, the externalization score (51.65±10.14, 48.33±8.97, respectively; t=2.02, p<0.05), social problem score (55.41±6.11, 53.24±5.19, respectively; t=2.27, p<0.05), and rule breaking behavior score (55.71±6.11, 53.24±5.19, respectively; t=2.27, p<0.05) were significantly higher in the screen overuse group than in the control group. In addition, the screen overuse group also had a significantly higher usage rate than the control group, even if limited to smartphones, not only on weekdays (3.56±2.08, 1.87±2.02, respectively; t=-4.597, p<0.001) but also weekends (1.62±0.74, 1.19±0.83, respectively; t=-3.14, p=0.003). Conclusion: The study suggested that screen media overuse patterns in children in Korea are particularly relevant to the excessive use of smartphones and are related to higher risks of emotional and behavioral problems.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.318-333
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• 2021

Smartphones are one of the most widely used mobile devices allowing users to communicate with each other. With the development of mobile apps, many companies now provide various services for their customers by studying interactive systems in the form of mobile messengers for business marketing and commercial promotion. Such interactive systems are called "chatbots." In this paper, we propose a method of avoiding the redundant responses of chatbots, according to the utterances entered by the user. In addition, the redundant patterns of chatbot responses are classified into three categories for the first time. In order to verify the proposed method, a chatbot is implemented using Telegram, an open source messenger. By comparing the proposed method with an existent method for each pattern, it is confirmed that the proposed method significantly improves the redundancy avoidance rate. Furthermore, response performance and variation analysis of the proposed method are investigated in our experiment.