• Electronics and Telecommunications Trends
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• v.33 no.1
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• pp.123-130
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• 2018

Tactile sensors, which are commonly referred to as pressure and strain sensors, have been extensively investigated to meet the demands for attachable and wearable electronics for monitoring the health status or activity of human users. For this purpose, the introduction of two-dimensional (2D) materials such as graphene and transition metal dichalcogenides (TMDs) with high mechanical strength at the atomic scale is very suitable for tactile sensors applicable for use in human-friendly devices. In this paper, we examine a descriptive summary of a tactile sensor and review state-of- the-art research trends of 2D material-based tactile sensors in terms of the material and architecture. Finally, we propose a roadmap for future studies into advanced tactile sensors based on our ongoing research.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.119-124
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• 2016

According to the expansion of smartphone penetration and development of wearable device, personal context information can be easily collected. To use this information, the context aware recommender system has been actively studied. The key issue in this field is how to deal with the context information, as users are influenced by different contexts while rating items. But measuring the similarity among contexts is not a trivial task. To solve this problem, we propose context aware post-filtering to apply the context compensation. To be specific, we calculate the compensation for different context information by measuring their average. After reflecting the compensation of the rating data, the mechanism recommends the items to the user. Based on the item recommendation list, we recover the rating score considering the context information. To verify the effectiveness of the proposed method, we use the real movie rating dataset. Experimental evaluation shows that our proposed method outperforms several state-of-the-art approaches.

• International Journal of Contents
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• v.4 no.2
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• pp.1-6
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• 2008

Pre schools with state of the art facilities that would provide not just academic excellence but also ensure the safety and provide efficient healthcare to their pupils relative to Atopic Dermatitis with Asthma is the main objective of this research One of the most promising applications of sensor networks is for human healthcare monitoring. Due to recent technological advances in sensor, low power microelectronics and miniaturization, and wireless networking enable the design and proliferation of this wireless sensor networks capable of autonomously monitoring and controlling environments. Thus, this research presents the utilization of such microelectronic sensor and plots the hardware and software architecture of a wireless sensor network system with real-time pupil monitoring that integrates vital sign sensors, location sensor and allergen sensor. This proposed architecture for wearable sensors can be used as active tags which can track pupil's location within the school's premises, identify possible atopic dermatitis with asthma allergens, it would monitor and generate a health status report of the pupil.

• Electronics and Telecommunications Trends
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• v.30 no.4
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• pp.1-11
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• 2015

Quantified Self란 개인의 일상활동에서 신체적 정신적 상태를 센싱 및 트래킹하여 이를 수치화함으로써 자신의 상태를 분석하고 삶의 질을 개선하기 위한 방법을 연구하여 실생활에 적용하는 활동을 의미한다. 이는 최근 헬스와 피트니스에 활용 가능한 개인 센서 및 웨어러블 기기의 급속한 보급과 다양한 개인 정보 트래킹 기기의 출현으로 개인의 일상경험을 모니터링하고, 생성되는 정보를 수집, 통합 분석을 통해 새로운 개인화 서비스를 제공하기 위한 기술개발 이슈로 주목받고 있다. Quantified Self 데이터는 초기의 수작업으로 트래킹하여 수집한 소량의 관리 가능한 데이터 세트에서 점차 대용량의 Quantified Self 빅데이터 세트로 크기가 증가하고 있으며, 개인정보의 통합분석을 위한 빅데이터 모델과 자동적인 셀프-트래킹 플랫폼으로서의 웨어러블 컴퓨터 기술과 응용의 기대치를 증가시키고 있다. 본고에서는 Quantified Self 정의와 기술 및 서비스 동향에 대해 살펴보고, 웨어러블 센싱 기반 트래킹 기기의 개발사례와 Quantified Self의 주요이슈와 미래전망에 대해 조망해 본다.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.635-643
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• 2015

This study conducted a case study using various platforms centered on a tennis game to examine what forms the movements of a game player had when they were abstracted in the game. In particular, it summarized the forms of the player's experience that could be attained from the abstracted tennis actions into the 4 types: movement, swing, direction & intensity, and skill; and observed and schematized them in the early video games, console games, mobile games, Gesture recognition games, and wearable games. In conclusion, the development of technology offers the players with greater experience. For example the change of the platform of simple games of pressing buttons into swinging. Furthermore, the study found a consistency in the context even though the difference of action was slightly found by the interface.

• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.19-26
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• 2020

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.547-548
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• 2014

ICT 기술발전으로 웨어러블 디바이스(wearable device)와 증강현실(augmented reality: AR) 및 가상현실(virtual reality: VR)등은 차세대 게임 산업에 새로운 재미와 가치를 부여할 기술로 주목받고 있다. 특히 가상현실 기술은 몰입(immersion)이라는 경험적 속성을 가지고 있어, 컴퓨터 시뮬레이션으로 창출된 3차원 가상공간으로 사용자의 오감 경험을 확장하고 공유함으로서 공간적, 물리적 제약에 의해 현실 세계에서는 직접 경험하지 못하는 상황을 간접 체험할 수 있도록 한다. 따라서 본 논문에서는 가상증강현실을 이용하여 유저와 상호작용하고, 사용자에게 현실적인 공포감을 줄 수 있도록 하는 오감 체험 공포 시뮬레이션을 개발하였다. 이 체험 시뮬레이션 게임은 Eon Studio 개발툴과 JavaScript를 이용하여 구현되었으며, Eon I-CUBE안에서 1인칭 주인공 시점으로 게임을 진행하도록 하였다.

• Science of Emotion and Sensibility
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• v.26 no.2
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• pp.117-128
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• 2023

This study aimed to build on past findings about differences in personal walking experiences by demonstrating what elements were beneficial to participants with different walking habits. Accordingly, this study established the relationships between valued walking elements and people's motivation to walk, by dividing participants into three groups: Group W for people with a walking habit, Group HW for people who walk occasionally but not regularly, and Group NW for people who do not walk habitually. Participants walked a familiar and an unfamiliar route with a wearable device that recorded their heart-rate variability and electrodermal activity. Changes in the biometric data helped to identify the defining moments in each participant's walk. Participants discussed these moments in one-on-one interviews with a researcher to pinpoint their valued walking elements. As a result, this study classified walking elements into six themes: "Surroundings," "Social," "Exploration," "Route Plan," "Physical Exercise," and "Mental Thinking." A walking habit development model was made to show how "Route Plan" and "Exploration" were beneficial to Group NW, "Social" and "Surroundings" were beneficial to Group HW, and "Route Plan," "Mental Thinking," and "Physical Exercise" were beneficial to Group W.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.10
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• pp.865-878
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• 2017

To realize the healthy life of human, mental, physical, and environmental factors must be managed continuously and stably. In order to manage human health, the 21st century healthcare field is essential ongoing interactions and convergence with ICT technologies. Such demands have created a convergence of technologies (fusion technology) in combination with the heterogeneous technologies. And, with the convergence of medical technology and ICT technologies, the development of personalized therapy environments is created. Advances in ICT-converged healthcare services are progressing due to the development of diverse wearable devices. Such ICT fusion system is exponentially increasing the complexity of the ICT convergence healthcare system and is resulting in various technical, institutional, environmental, and cultural issues. This study explores the status of developments in ICT healthcare technologies from the past to date, identifies major technology and policy issues to address these challenges. Finally it will recommend healthcare policies and a future road-map.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.