• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.365-375
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• 2012

This study aimed to survey the impact of smartphone overuse on upper extremity pain, anxiety, depression, and interpersonal relationships among college students. Subjects completed a structured questionnaire consisting of the Smartphone Addiction Inventory, the Musculoskeletal Symptom Checklist, the State-Trait Anxiety Inventory, the Beck Depression Inventory-II, and the Relationship Change Scale from May to June 2012. We analyzed the survey data from 525 responses, excluding unreturned or incomplete surveys. Data were analyzed using the $x^2$ test and t-test to determine the differences in smartphone overuse and its impact on upper extremity pain, anxiety, depression, and interpersonal relationships between two groups: the overuse and normal use groups. Moreover, Pearson's correlation coefficient was used to examine the correlation between smartphone overuse and upper extremity pain, anxiety, depression, and interpersonal relationships. The results placed 62 people (11.8%) in the smartphone overuse group. The extent of smartphone overuse was more severe among female than male college students, and longer time spent using smartphones per day was directly related to smartphone overuse. The smartphone overuse group evidenced higher shoulder pain than the normal use group did, but no differences were found in other sites of the upper extremities. State anxiety, trait anxiety, and depression were higher in the smartphone overuse group than in the normal use group. Subjects with a higher extent of smartphone overuse experienced increased state-anxiety, trait-anxiety, and depression. Moreover, subjects with higher state-anxiety, trait-anxiety, and depression scores were more likely to have poor interpersonal relationships. Therefore, early screening for smartphone overuse should be evaluated, because it can be useful in developing addiction prevention programs to improve posture, stress coping, positive mental health, and effective interpersonal relationships.

• Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.153-160
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• 2014

As smartphones came into wide use recently, it has become increasingly popular not only among young people, but among middle-aged people as well. Most smartphones adopt capacitive full touch screen, so touch commands are made by fingers unlike the PDAs in the past that use touch pens. In this case, a significant portion of the smartphone's screen is blocked by the finger so it is impossible to see the screens around the finger touching the screen; this causes difficulties in making precise inputs. To solve this problem, this research proposes a method of using simple AR markers to improve the interface of smartphones. A marker is placed in front of the smartphone camera. Then, the camera image of the marker is analyzed to determine the position of the marker as the position of the mouse cursor. This method can enable click, double-click, drag-and-drop used in PCs as well as touch, slide, long-touch-input in smartphones. Through this research, smartphone inputs can be made more precise and simple, and show the possibility of the application of a new concept of smartphone interface.

• Korean Journal of Computational Design and Engineering
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• v.16 no.5
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• pp.361-369
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• 2011

As smartphones came into wide use recently, it has become increasingly popular not only among young people, but middle-aged people as well. Most smartphones use capacitive full touch screen, so touch commands are made by fingers unlike the PDAs in the past that use touch pens. In this case, a significant portion of the smartphone's screen is blocked by the finger so it is impossible to see the screens around the finger touching the screen, and difficulty occurs in precise control used for small buttons such as qwerty keyboard. To solve this problem, this research proposes a method of using simple AR markers to improve the interface of smartphones. Sticker-form marker is attached to fingernails and placed in front of the smartphone camera Then, the camera image of the marker is analyzed to determine the orientation of the marker to perceive as onRelease() or onPress() of the mouse depending on the marker's angle of rotation, and use its position as the position of the mouse cursor. This method can enable click, double-click, drag-and-drop used in PCs as well as touch, slide, long-touch-input in smartphones. Through this research, smartphone inputs can be made more precise and simple, and show the possibility of the application of a new concept of smartphone interface.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.49-54
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• 2013

Activity recognition using smartphone accelerometer suffers from the user dependency problem that acceleration patterns of one user differ from those of others for the same activity. Moreover, it also suffers from the position dependency problem since a smartphone may be placed in any pockets or hands. In order to overcome these problems, this paper proposes an effective activity recognition method which is less dependent with both specific users and specific positions of the smartphone. Based on the proposed method, we implement a real-time activity recognition system working on an Android smartphone. Throughout some experiments with 6642 examples collected from different users and different positions, we investigate the performance of our activity recognition system.

• Women's Health Nursing
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• v.26 no.2
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• pp.191-199
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• 2020

Purpose: The purpose of this study was to investigate the effects of anxiety and smartphone dependency on sleep quality in pregnant women with preterm labor. Methods: The participants of this study were 111 pregnant women who were between 20 and 37 weeks of gestation and experienced preterm labor. The data were collected from October 1, 2018 to October 25, 2019. The collected data were analyzed using descriptive statistics (frequency, percentage, and standard deviation), as well as the t-test, Pearson correlation coefficients, and hierarchical multiple regression. Results: Significant negative correlations were found between anxiety and sleep quality and between smartphone dependency and sleep quality. Participants' history of preterm birth, pregnancy method, bowel movements, anxiety, and smartphone dependency significantly affected sleep quality, with an explanatory power of 18%. Conclusion: In order to improve the quality of sleep, which is an important health-related factor for pregnant women experiencing preterm labor, it will be necessary to identify a history of premature birth, pregnancies achieved using artificial reproductive technology, bowel problems, and smartphone dependency in advance and to provide nursing interventions accordingly.

• Journal of Korea Artificial Intelligence Association
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• v.2 no.1
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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.

• Journal of IKEEE
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• v.19 no.2
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• pp.237-243
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• 2015

Recently, eight out of ten people have smartphone in Korea. Also, many applications of smartphone have increased. So, smartphone addiction has become a social issue. Especially, many people in smartphone addiction can't control themselves. Sometimes they don't realize that they are smartphone addiction. Many studies, mostly surveys, have been conducted to diagnose smartphone addiction, e.g. S-measure. In this paper, we suggest how to detect smartphone addiction based on ECG and Eye Gaze. We measure the signals of ECG from the Shimmer and the signals of Eye Gaze from the smart eye when the subjects see the emotional video. In addition, we extract features from the S-transform of ECG. Using Eye Gaze signals(pupil diameter, Gaze distance, Eye blinking), we extract 12 features. The classifier is trained using Random Forest. The classifiers detect the smartphone addiction using the ECG and Eye Gaze signals. We compared the detection results with S-measure results that surveyed before test. It showed 87.89% accuracy in ECG and 60.25% accuracy in Eye Gaze.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.398-407
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• 2017

This study examined the relationship among smartphone addiction, life stress, and family communication in nursing students. A total of 172 subjects participated in this study and the data were collected using the structured questionnaires. The collected data were analyzed using an independent t-test, ANOVA, Pearson's correlation coefficient, and multiple linear regression using the SPSS WIN 19.0 program. Overall, the level of stress was high, time of family communication was short, and smartphone addiction rate was high in nursing students. The high risk group of smartphone addiction was 14.0%, potential risk group was 29.0%, and normal group was 57.0%. In the risk group, the life stress was higher (t=3.15, p=.002) and family communication was not better (t=-2.53, p=.012) than the normal group. Smartphone addiction correlated significantly with life stress (r=.27, p<.001) and family communication (r=-.26, p=.001). The factors affecting smartphone addiction were smartphone usage time, life stress, importance of smartphones in their lives, and family communication, and the explanatory power was 31.3%. Based on these results, it is necessary to develop personalized and collective customized intervention programs focused on smartphone using method, life stress management, and family communication for the prevention and management of smartphone addiction of nursing students.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.1-8
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• 2013

Smartphone enables diverse applications to be used in mobile environments. In spite of the high performance of smartphones, battery life has become one of the major constraints in mobility. Therefore, power efficiency of the smartphone is one of the most important factors in determining the efficiency of the smartphone. In this paper, in order to analyze the power efficiency of the smartphone, we have various experiments according to several configuration parameters such as processor, display and OS. We also use diverse applications. As a result, power consumption is dependent on the processor complexity and display size. However, power consumption shows the unpredictable pattern according to the OS. Smartphone using android OS consumes high power when internet and image processing applications are executed, but It consumes low power when music and camera applications are executed. In contrary, smartphone based on iOS consumes high power when game and internet applications are executed but it consumes low power when camera and processing applications are executed. In general, smartphone using iOS is more power efficient than smartphone based on android OS, because smartphone using iOS is optimized in the perspective of the hardware and OS.

• Child Health Nursing Research
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• v.23 no.4
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• pp.525-533
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• 2017

Purpose: The purpose of this study was to verify the relationship among depression, school adjustment, parent-child bonding, parental control and smartphone addiction, and to identify factors which influence smartphone addiction in adolescents. Methods: A cross-sectional design was used, with a convenience sample of 183 middle school students from 3 middle schools. Data collection was conducted through self-report questionnaires from April to May, 2017. Data were analyzed using ${\chi}^2$ test, Fisher's exact test, t-test, one-way ANOVA, correlation coefficient analysis, and binary logistic regression with SPSS Ver. 21.0. Results: The mean score for smartphone addiction was 29.40. Of the adolescents, 21.3% were in the smartphone addiction risk group. Logistic regression analysis showed that gender (OR=7.09, 95% Cl: 2.57~19.52), school life (OR=0.86, 95% Cl: 0.79~0.93), smartphone usage time (OR=1.32, 95% Cl: 1.04~1.66), and parental control (OR=4.70, 95% Cl: 1.04~21.29) were effect factors for the smartphone addiction risk group. Conclusion: Findings indicate that school satisfaction was an important factor in adolescents' smartphone addiction. Control oriented parent management of adolescents' smartphone use did not reduce the risk of smartphone addiction and may have worsen the addiction. Future research is needed to improve understanding of how teachers and parents will manage their adolescents' use of smartphones.