• Journal of The Korean Society of Integrative Medicine
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• v.8 no.3
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• pp.11-20
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• 2020

Purpose : The purpose of this study was to determine the correlation between smartphone use, body composition, posture and muscle properties of elementary school students. Methods : A total of 22 healthy elementary school students in Daejeon, South Korea, participated in this study with informed consent. Smartphone use was evaluated with Smartphone addiction self-report scale (S-scale) for youth. Body composition (BMI, percent body fat, body fat mass, and skeletal muscle mass) was measured using Inbody. To assess posture, forward head posture and round shoulder posture were measured using Dartfish motion analysis. Muscle properties (muscle tone, stiffness, and elasticity) were measured on the upper trapezius, splenius capitis and sternocleidomastoid muscles using Myoton. To investigate the correlation between the main variables, Pearson correlation analysis was performed. Results : Disturbance of adaptive function, which is one of sub-component of S-scale, had a weak positive correlations with BMI (r=.538, p<.01), and body fat mass (r=.453, p<.05). However, scores of S-scale showed no correlation with posture and muscle properties. The correlation between posture and muscle properties was analyzed, and round shoulder posture revealed a weak positive correlation with respect to stiffness (r=.418, p<.05) and elasticity (r=.502, p<.05) of the sternocleidomastoid muscle. Conclusion : Scores of S-scale demonstrated a weak correlation with body composition (BMI) among elementary school students, whereas S-scale scores were not correlated with the posture and muscle properties. Further research is needed to identify and focus on potential high-risk user groups of elementary students who are more dependent upon smartphones.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.37-43
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• 2020

Smartphone-based localization has been widely studied in many different scenarios. But as far as we know, few work has addressed the problem of localization in underground public transportation systems, where GPS signal and wireless infrastructure are not always available. Knowing the location of a train is necessary to develop a useful service for subway passengers. And so, estimation of motion state and stop station by using sensors on a smartphone is being studied for subway passengers. This paper proposes a localization method that uses a barometer and a magnetic sensor on a smartphone. The method proposed in this paper first estimates whether the train runs or stops according to the change in air pressure and the strength of the magnetic field. The altitude value and the magnetic field value are then used to estimate the exact stop station of the train. We evaluated the proposed method using data from the Seoul's subway line 5. Compared with previous methods, the proposed method achieves higher accuracy.

• Journal of Korean Society of Forest Science
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• v.110 no.1
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• pp.72-81
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• 2021

We developed smartphone application and web application server to acquire and effectively manage tree measurement information. Smartphone applications can measure tree height, diameter at breast height (DBH), azimuth, altitude, slope, and positional coordinates using augmented reality (Google AR core) and motion sensors. The web application server effectively manages and stores measurement information. To evaluate the accuracy of information acquired using a smartphone, 90 Korean pine trees (Pinus koraiensis) were randomly selected from a natural mixed forest, with a total of 90 representative trees randomly collected from a natural mixed forest. Then, height and DBH were measured using a Haglof Vertex Laser Hypsometer and caliper. Comparisons of the results indicated significant results at the 95% level and a very high average correlation of 0.972 for both tree height and DBH. In terms of DBH, the average errors were 0.6745 cm and 1.0139 cm for artificial coniferous and natural mixed forests, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.93-98
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• 2023

Recently, as the size of display devices tends to increase and taking pictures with smart phones has become commonplace, the need for taking high-resolution pictures with smart phones is increasing. However, when the lens size of a camera is limited, such as in a smartphone, there is a physical limit to increasing the resolution of a photo. This paper is about a technique for increasing the resolution of a picture even when using a small-sized lens like a smartphone camera. It is to take multiple pictures while moving the smartphone, and to increase the resolution by combining these pictures into one picture. First of all, two pictures were taken while moving the smartphone horizontally for the 2D picture. Processes such as camera matrix estimation, and homograph inverse transformation were performed using OpenCV, and the resolution was improved by synthesizing one picture. It was confirmed that the resolution was improved in parts such as oblique lines or arcs on several test pictures.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.6
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• pp.164-172
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• 2023

The grain size distribution of sand provides crucial information for understanding coastal erosion and sediment deposition. The commonly used sieve analysis for grain size distribution analysis has limitations such as time-consuming processes and the inability to obtain information about individual particle shapes and colors. In this study, we propose a grain size distribution analysis method using smartphone digital images, which is simpler and more efficient than the sieve analysis method. During the image analysis process, we effectively detect particles from relatively low-resolution smartphone digital images by extracting particle boundaries through image gradient calculation. Using samples collected from four beaches in Gyeongsangbuk-do, we compare and validate the proposed boundary extraction image analysis method with the analysis method that does not extract boundaries, against sieve analysis results. The proposed method shows an average error rate of 8.21% at D₅₀, exhibiting a 65% lower error compared to the method without boundary extraction. Therefore, grain size distribution analysis using smartphone digital images is convenient, efficient, and demonstrated accuracy comparable to sieve analysis.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.6
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• pp.323-334
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• 2014

In this paper, we proposed indoor positioning system with improved accuracy. The proposed indoor location measurement system is based pedestrian location measurement method that use the embedded sensor of smartphone. So, we do not need wireless external resources, such as GPS or WiFi signals. The conventional methods measure indoor location by generating a movement route of pedestrian by step and direction recognition. In this paper, to correct the direction sensor error, we use the common feature of the normal indoor floor map that the indoor path is lattice-structured. And we quantize moving directions depending on the direction of indoor path. In addition, we propose moving direction measuring method using geomagnetic sensor and gyro sensor to improve the accuracy. Also, the proposed step detection method uses angle and accelerometer sensors. The proposed step detection method is not affected by the posture of the smartphone. Direction errors caused by direction sensor error is corrected due to proposed moving direction measuring method. The proposed location error correction method corrects location error caused by step detection error without the need for external wireless signal resources.

• Korean Journal of Health Education and Promotion
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• v.34 no.1
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• pp.13-24
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• 2017

Objectives: This study is to investigate the sexual behaviors among smartphone users of middle school male students. Methods: Data was collected on all students in one middle school of seoul city using a structured questionnaire that is consisted of the detailed fifty-one questions for the five variables, the general characteristics, the features of smartphone obscenity use, sexual knowledge, sexual attitudes and sexual behaviors from Sep. 24 to Oct. 25 in 2015. The responded 480 questionnaires were analyzed by using the SPSS(Statistical Package for the Social Science) WIN 21.0 Program. Results: The results from this study showed that the variables which affected sexual behaviors were school record(high), watching frequency(2-8 times/month, more than 9 times/month), watching reason(solving sexual desire), sexual attitudes, of which sexual attitude was a most influential variable. Therefore, to establish healthy sexual behaviors, it is required to raise correct sexual attitudes. and means that school sex education should make a way of establishing correct sexual attitudes. Conclusions: Professional and systematical sexual education is needed to increase responsible sexual attitude on sexual behaviors.

• Child Health Nursing Research
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• v.22 no.4
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• pp.346-354
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• 2016

Purpose: The purpose of this study was to investigate the use of smartphones to address parents' educational needs. Methods: The participants were parents of children under 18 years old, Participants were 347 parents (222 mothers, 125 fathers). Data were collected from August, 15 to September, 2, 2016 using self-report questionnaire. Data were analyzed using descriptive statistics, t-test and ANOVA with the SPSS WIN 21.0 program. Results: Parents had an average score of 3.30 (${\pm}.41$) for the need to have smartphone health education. Among the educational subcategories, 'child and family' showed the highest average for educational needs (3.40, ${\pm}57$). According to the demographic characteristics of the participants, there were significant difference in parents' age (t=4.354, p=.014), and parents' who have received health education versus parents who have not (t=2.483, p=.014). Conclusion: Findings indicate that the smartphone may be an effective educational method for parents.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.39-47
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• 2016

This study is a smart tourism research based on face recognition applied system that manages individual information of foreign tourists to smartphone. It is a way to authenticate by using face recognition, which is biometric information, as a technology applied to identification inquiry, immigration control, etc. and it is designed so that tourism companies can provide customized service to customers by applying algorism to smartphone. The smart tourism system based on face recognition is a system that prepares the reception service by sending the information to smartphone of tourist service company guide in real time after taking faces of foreign tourists who enter Korea for the first time with glasses attached to the camera. The smart tourism based on face recognition is personal information recognition technology, speech recognition technology, sensing technology, artificial intelligence personal information recognition technology, etc. Especially, artificial intelligence personal information recognition technology is a system that enables the tourism service company to implement the self-promotion function to commemorate the visit of foreign tourists and that enables tourists to participate in events and experience them directly. Since the application of smart tourism based on face recognition can utilize unique facial data and image features, it can be beneficially utilized for service companies that require accurate user authentication and service companies that prioritize security. However, in terms of sharing information by government organizations and private companies, preemptive measures such as the introduction of security systems should be taken.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.201-209
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• 2017

This paper is a smartphone object recognition system using hierarchical convolutional neural network. The overall configuration is a method of communicating object information to the smartphone by matching the collected data by connecting the smartphone and the server and recognizing the object to the convergence neural network in the server. It is also compared to a hierarchical convolutional neural network and a fractional convolutional neural network. Hierarchical convolutional neural networks have 88% accuracy, fractional convolutional neural networks have 73% accuracy and 15%p performance improvement. Based on this, it shows possibility of expansion of T-Commerce market connected with smartphone and broadcasting media.