• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.469-472
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• 2020

본 논문에서는 립모션 디바이스를 이용하여 손의 움직임을 계산하고 이로부터 저글링 운동 동작뿐 만 아니라 이것을 이용한 팔 근육을 연습할 수 있는 새로운 프레임워크를 제안한다. 제안된 방법은 실시간으로 동작하기 때문에 사용자의 동작에 맞춰진 분석을 할 수 있다. 본 논문의 프레임워크는 크게 세 부분으로 나누어진다. 우선, 1) 사용자가 공을 튕기는 이벤트 트리거를 손목 움직임으로부터 정의한 뒤, 2) 사용자의 손 위치를 기준으로 저글링 형태의 움직임을 공에 매핑시키기 위한 포물선 기반 입자 기법을 제안한다. 마지막으로, 3) 손목의 굽힘을 기반으로 근육의 활동 양을 시각화할 수 있는 기법을 제안한다. 결과적으로 본 논문의 프레임워크를 이용하면 실시간 저글링 게임을 할 수 있을 뿐만 아니라 사용자의 팔 근육 움직임을 실시간으로 분석할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.534-535
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• 2020

본 논문에서는 딥러닝과 지식베이스를 융합하여 활용한 질환 인식 및 식품 추천 시스템을 제안한다. 제안하는 시스템은 온전히 모바일 디바이스 내에서 작동하는 시스템이다. 본 시스템은 압축된 딥러닝 모델을 이용해 사용자 대화 텍스트를 분석하여 사용자의 질환을 예측한다. 그 후, 지식베이스를 기반으로 해당 질환 관리에 도움이 되는 식품을 매칭하고 사용자에게 추천한다. 이는 사용자 친화적 헬스케어 애플리케이션으로써 체크리스트 작성 등 번거로운 작업 없이도 사용자에게 유용한 건강 정보를 제공할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.88-89
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• 2023

기존의 헬스케어 분야에서 사물인터넷 기술은 단순히 사용자가 웨어러블 디바이스를 통해 개인적으로 건강 정보를 확인하고 응급 상황 시에 직접 응급 센터에 연락하는 등의 제한된 용도로만 사용되고 있다. 따라서, 본 연구에서는 신속한 응급 환자 식별 및 이송을 위해 사물인터넷 기반의 응급 환자 관리 시스템을 제안한다. 특히, 사용자의 응급 상황 1분 전후의 정보를 수집하여 119안전센터와 구급차로 전달하여 빠른 환자 상황 대처가 가능하게 한다. 또한, 실시간 구급차의 이동상황에 따라 신호등 상태를 조작하여 응급 환자를 보다 빨리 병원으로 이송한다.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.33-39
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• 2023

As the number of people with chronic diseases increases due to an aging society, it is urgent to prevent and manage their diseases. Although biometric authentication methods and Telemedicine Systems have been introduced to solve these problems, it is difficult to solve the security problem of medical information and personal authentication. Since smart healthcare includes personal medical information of subjects, the security of personal information is the most important field. Therefore, in this paper, we tried to propose a Telemedicine System using a smart wearable device ECG in the form of a wristband and face personal authentication in a private blockchain environment. This system targets various medical personnel and patients with chronic diseases in all regions, and uses a private blockchain that can increase data integrity and transparency, ECG and face authentication that are difficult to forge and alter and have high personal identification to provide a system with high security and reliability. composed. Through this, it is intended to contribute to increasing the efficiency of chronic disease management by focusing on disease prevention and health management for patients with chronic diseases at home.

• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.151-156
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• 2020

The purpose of this study is to design a process for developing wearable sports helmet devices by utilizing IoT server technology, focusing on sports where helmet wear is essential at sports sites. This enables customized training of athletes by continuously accumulating personal biometric information during training, checking players' condition based on data, and informing them of injury prevention and dangerous situations. In addition, the wearable device that can be useful when the training place is likely to damage the physical health due to heat waves or extremes can provide a foundation for improving the performance. Since such technology can be applied not only to the sports field but also to the society such as the industrial field or the underprivileged, it can be expected to be expandable.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.97-110
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• 2022

Federated learning is a learning method capable of performing model learning without transmitting learning data. The IoT or healthcare field is sensitive to information leakage as it deals with users' personal information, so a lot of attention should be paid to system design, but when using federated-learning, data does not move from devices where data is collected. Accordingly, many federated-learning implementations have been developed, but detailed research on system design for the development and operation of systems using federated learning is insufficient. This study shows that measures for the life cycle, code version management, model serving, and device monitoring of federated learning are needed to be applied to actual projects and distributed to IoT devices, and we propose a design for a development environment that complements these points. The system proposed in this paper considered uninterrupted model-serving and includes source code and model version management, device state monitoring, and server-client learning schedule management.

• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.86-92
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• 2022

Recently, the bio-healthcare market is enlarging worldwide due to various reasons such as the COVID-19 pandemic. Among them, biometric measurement and analysis technology are expected to bring about future technological innovation and socio-economic ripple effect. Existing systems require a large-capacity battery to drive signal processing, wireless transmission part, and an operating system in the process. However, due to the limitation of the battery capacity, it causes a spatio-temporal limitation on the use of the device. This limitation can act as a cause for the disconnection of data required for the user's health care monitoring, so it is one of the major obstacles of the health care device. In this study, we report the concept of a standalone healthcare monitoring module, which is based on both triboelectric effects and electromagnetic effects, by converting biomechanical energy into suitable electric energy. The proposed system can be operated independently without an external power source. In particular, the wireless foot pressure measurement monitoring system, which is rationally designed triboelectric sensor (TES), can recognize the user's walking habits through foot pressure measurement. By applying the triboelectric effects to the contact-separation behavior that occurs during walking, an effective foot pressure sensor was made, the performance of the sensor was verified through an electrical output signal according to the pressure, and its dynamic behavior is measured through a signal processing circuit using a capacitor. In addition, the biomechanical energy dissipated during walking is harvested as electrical energy by using the electromagnetic induction effect to be used as a power source for wireless transmission and signal processing. Therefore, the proposed system has a great potential to reduce the inconvenience of charging caused by limited battery capacity and to overcome the problem of data disconnection.

• Design Convergence Study
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• v.16 no.6
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• pp.123-135
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• 2017

In recent mobile health care service, health management using number of steps is becoming popular. In addition, a variety of activity trackers have made it possible to measure the number of steps more accurately and easily. Nevertheless, the activity tracker is not popularized, and it is a trend to use the pedometer sensor of the smartphone as an alternative. In this study, we tried to find out how much the number of steps collected by the smartphone versus the actual number of steps in actual situations, and what factors make the difference. We conducted an experiment to collect number of steps data of 21 people using the smartphone and wearable device simultaneously for 7 days. As a result, we found that the average number of steps of the smartphone is 62% compared to the actual number of steps, and that there is a large variation among users. We derived a regression model in which the accuracy of smartphone increases with the degree of awareness of smartphone. We expect that this can be used as a factor to correct the difference from the actual number of steps in the smartphone alone healthcare service.

• Journal of IKEEE
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• v.23 no.3
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• pp.844-851
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• 2019

As wearable technology is becoming more common and a part of our lives, there have been many efforts to offer various smart services with wearable devices, such as motion recognition, safety of driving, and so on. In this paper, we present a method that exploits the 9-axis inertial sensors embedded in a smartwatch to identify whether the user is a vehicle driver or not and to estimate the steering wheel turning direction in the vehicle. The system consists of three components: (i) position recognition, (ii) driver recognition, and (iii) steering-wheel turning detection components. We have developed a prototype system for detecting user's motion with Arduino boards and IMU sensors. Our experiments show high accuracy in recognizing the driver and in estimating the wheel rotation angle. The average experimental error was $11.77^{\circ}$ which is small enough to perceiver the turning direction of steering-wheel.

• Convergence Security Journal
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• v.22 no.1
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• pp.135-140
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• 2022

This study aims to analyze exercise cases and issues using smart devices and technologies, and to present the development direction of a smart exercise environment suitable for the wellness life of active seniors with high activity and economic power unlike the existing silver generation. In the fitness industry, the subscription economy that regularly receives or uses necessary exercise tools, services, and digital content is expanding, and business models based on hardware sales and content subscription continue to emerge. In order to have value competitiveness as a platform that provides active seniors with integrated exercise services for health care, not only fitness centers, but also home training exercise equipment, fitness-related applications, and smart wearable device markets should be organically connected to form an expanded total platform. In order to have value competitiveness as a platform that provides active seniors with integrated exercise services for health care, not only fitness centers, but also home training exercise equipment, fitness-related applications, and smart wearable device markets should be organically connected to form an expanded total platform. The linkage of the digital healthcare function, which provides real-time changes to exercise programs based on continuous monitoring and feed back through wearable devices before, after, and during exercise by receiving and selecting exercise programs suitable for individual health status, is the differentiating factor in the smart fitness model.