• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.129-135
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• 2017

In this paper, we propose a system to monitor the condition of elderly people who are uncomfortable by using smart-phone and biometric sensor at any time and for care-givers to provide with the best medical service anytime and anywhere. The proposed system monitors the status of the elderly through various bionic sensors installed in the hospital ward based on the Arduino system and not only provides the physiological and medical services required by the elderly, but also informs the guardian so that he can cope with the emergency. In conclusion, this paper suggests that a reading light used by elderly people can operate as a home server with a biosensor using Arduino and Android applications (Apps.), and the smart-phone of care-givers is configured as a remote management and a emergency call system. Therefore, this study suggests important ways to improve the satisfaction of medical service for the both elderly people and care-givers of chronic diseases in the future.

• Journal of Practical Engineering Education
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• v.6 no.2
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• pp.135-139
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• 2014

In this study, the new model of presentation remote controller in which has improved the conventional function and deteceted the level of human's tension on a real time basis is suggested and tested. Existing presentation remote controller was just used turning the pages. But new model controls presentation and check tension level on real time using the smart phone's bluetooth interface. The proposed system is comprised with the PPG (Photo-Plethysmo-Graphy) sensor, Bluetooth and Wi-Fi modules. The configured system is to process (within 150 ms) the pulse signals of the presenter and stored the data. As a result, it can check and make up for the week presentation part and used as sources for improving self-confidence. This is the result obtained from the process of capstone design irregular course for 20 weeks of a graduate-to-be in four-year college.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.993-995
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• 2014

Typical dwelling pattern of apartment houses in Korea. Because of this the noise of life problem arise, complaints are surging. In real-life, if suffering is unavoidable due to ambient noise, to handle a civil complaint the using a noise meter. At home, it is difficult to measure the noise using professional equipment. So, many uses smartphone application in general. But released existing noise measurement application has different value from the sensor sensitivity for each smartphone model to same situation. The value is lacks precision and this is not considered as having been made by measuring the actual noise purpose. Therefore in this paper, we propose a mobile data processing system for the living environment of noise measurement using a smartphone. Benefits of this study is to improve the accuracy of noise measurements and to find direction of noise to handle complaints.

• Smart Media Journal
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• v.8 no.3
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• pp.88-94
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• 2019

Livestock disease is a very important issue in the livestock industry because if livestock disease is not responded quickly enough, its damage can be devastating. To solve the issues involving the occurrence of livestock disease, it is necessary to diagnose in advance the status of livestock disease and develop systematic and scientific livestock feeding technologies. However, there is a lack of domestic studies on such technologies in Korea. This paper, therefore, proposes Livestock Disease Forecasting and Livestock Farm Integrated Control System using Cloud Computing to quickly manage livestock disease. The proposed system collects a variety of livestock data from wireless sensor networks and application. Moreover, it saves and manages the data with the use of the column-oriented database Hadoop HBase, a column-oriented database management system. This provides livestock disease forecasting and livestock farm integrated controlling service through MapReduce Model-based parallel data processing. Lastly, it also provides REST-based web service so that users can receive the service on various platforms, such as PCs or mobile devices.

• Journal of Information Technology Applications and Management
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• v.28 no.3
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• pp.59-75
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• 2021

This study aims to establish a foundation for autonomous driving on campus and communication of abundant university information in the HCI environment in a VR environment where college freshmen can freely travel around campus facilities. The purpose of this study is to develop a three-dimensional VR-style campus tour system to establish a media environment to provide abundant university information guidance services to freshmen in non-face-to-face situations. This study designed a three-dimensional virtual reality campus tour system to solve the problem of discontinuity in which VR360 filming space does not lead to space like reality, and to solve many problems of expertise in VR technology by constructing an integrated production environment of tour system. We aim to solve the problem of inefficiency, which requires a large amount of momentum in virtual space, by constructing a GUI that utilizes the motion of the field of view focus. The campus environment was designed as a three-dimensional virtual reality using a three-dimensional graphic design. In non-face-to-face situations, college freshmen freely transformed the HMD VR device, smartphone, FPS operation mode of the gyroscope sensor. The design elements of the three-dimensional virtual reality campus tour system were classified as ①Visualization of factual experiences, ②Continuity of space movement, ③Operation, automatic operation mode, ④Natural landscape animation, ⑤Animation according to wind direction, ⑥Actual space movement mode, ⑦Informatization of spatial understanding, ⑧GUI by experience environment, ⑨Text GUI by building, ⑩VR360, 3D360 Studio Environment, ⑪Three-dimensional virtual space coupling block module, ⑫3D360-3D Virtual Space Transmedia Zone, ⑬Transformable GUI(VR Device Dual Viewer-Gyro Sensor Full Viewer-FPS Operation Viewer) and an integrated production environment was established with each element. It is launched online (http://vautu.com/u1) by constructing a GUI for free driving mode and college information screens to adapt to college life for freshmen, and designing an environment that can be used simultaneously by current media such as PCs, Android, and iPads. Therefore, it conducted user research, held a development presentation, a forum on excellence in university innovation support projects, and applied it as a system on the website of a particular university. College freshmen will be able to experience university information directly from the web and app to the virtual reality campus environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.347-350
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• 2018

In order for an IoT system to automatically make the house temperature pleasant for the user, the system needs to predict the optimal start-up time of air-conditioner or heater to get to the temperature that the user has set. Predicting the optimal start-up time is important because it prevents extra fee from the unnecessary operation of the air-conditioner and heater. This paper introduces an ANN(Artificial Neural Network) and an IoT system that predicts the cooling and heating time in households using air-conditioner and heater. Many variables such as house structure, house size, and external weather condition affect the cooling and heating. Out of the many variables, measurable variables such as house temperature, house humidity, outdoor temperature, outdoor humidity, wind speed, wind direction, and wind chill was used to create training data for constructing the model. After constructing the ANN model, an IoT system that uses the model was developed. The IoT system comprises of a main system powered by Raspberry Pi 3 and a mobile application powered by Android. The mobile's GPS sensor and an developed feature used to predict user's return.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.118-131
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• 2012

The existing electronic tagging system traces the location of a sex offender through communicating with GPS satellites and mobile phone base stations in order to prevent repeated crimes. However, the GPS satellite communication method does not work well in the interiors of downtown buildings or on the subways where it is difficult to receive satellite signals. In such cases, the location can be traced through communication with mobile phone base stations. But the distance between mobile phone base stations is several hundred meters, and as a result the margin of error for location tracing can be maximum of 2km in accuracy reduction. Take for example, if a kindergarten is located on the 2nd floor and a coffee shop and the sex offender are located on the 3rd floor in a 5-story building that is downtown, the existing electronic tagging system cannot trace the location of the sex offender as the GPS satellite communication does not work in the interior of the building and the exact floor that the sex offender is located on cannot be recognized through communication with mobile phone base stations. This occurrence is a big problem for the existing electronic tagging system, which is based on position recognition. Therefore, this study suggests a smart electronic tagging system that can monitor sex offenders by using a Ubiquitous Sensor Network in such extreme situations where position recognition is not possible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.396-402
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• 2016

Recently, as the performance of smart phones has advanced and their distribution has increased, various functions in existing devices are accumulated. In particular, functions in smart devices have matured through improvement of diverse sensors. Various applications with the development of smart phones get fleshed out. As a result, services from applications promoting physical activity in users have gotten attention from the public. However, these services are about diet alone, and because these have no exercise motion recognition capability to detect movement in the correct position, the user has difficulty obtaining the benefits of exercise. In this paper, we develop exercise motion-recognition software that can sense the user's motion using a sensor built into a smart phone. In addition, we implement a system to offer exercise with friends who are connected via web server. The exercise motion recognition utilizes a Kalman filter algorithm to correct the user's motion data, and compared to data that exist in sampling, determines whether the user moves in the correct position by using a DTW algorithm.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.